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http://arxiv.org/pdf/2307.14700v1 Mathematical modelling and computational reduction of molten glass fluid flow in a furnace melting basin http://arxiv.org/pdf/2307.14045v1 Physics-Informed Neural Networks for Parametric Compressible Euler Equations http://arxiv.org/pdf/2307.13784v1 Modeling Turbulent Flows with LSTM Neural Network http://arxiv.org/pdf/2307.13659v1 Comparing phase-space and phenomenological modeling approaches for Lagrangian particles settling in a turbulent boundary layer http://arxiv.org/pdf/2307.13596v1 A method to extract macroscopic interface data from microscale rough/porous wall flow fields http://arxiv.org/pdf/2307.13538v1 INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations http://arxiv.org/pdf/2307.13533v1 Differentiable Turbulence II http://arxiv.org/pdf/2307.13517v1 Towards Long-Term predictions of Turbulence using Neural Operators http://arxiv.org/pdf/2307.13144v1 Reliable coarse-grained turbulent simulations through combined offline learning and neural emulation http://arxiv.org/pdf/2307.12650v1 Active Flow Control for Bluff Body Drag Reduction Using Reinforcement Learning with Partial Measurements http://arxiv.org/pdf/2307.12453v1 Multi-Fidelity Data Assimilation For Physics Inspired Machine Learning In Uncertainty Quantification Of Fluid Turbulence Simulations http://arxiv.org/pdf/2307.11419v1 Solving flows in porous media with a POD-Galerkin reduced order model coupled with multilayer perceptron http://arxiv.org/pdf/2307.09980v1 Adjoint-based machine learning for active flow control http://arxiv.org/pdf/2307.09142v1 Characterization of partial wetting by CMAS droplets using multiphase many-body dissipative particle dynamics and data-driven discovery based on PINNs http://arxiv.org/pdf/2307.09058v1 Physical interpretation of neural network-based nonlinear eddy viscosity models http://arxiv.org/pdf/2307.08981v1 A voxelized immersed boundary (VIB) finite element method for accurate and efficient blood flow 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Model with Operator Learning http://arxiv.org/pdf/2308.02293v2 A stochastic optimization approach to train non-linear neural networks with a higher-order variation regularization http://arxiv.org/pdf/2308.01729v1 Telematics Combined Actuarial Neural Networks for Cross-Sectional and Longitudinal Claim Count Data http://arxiv.org/pdf/2308.01475v1 Interpretable Machine Learning for Discovery: Statistical Challenges \& Opportunities http://arxiv.org/pdf/2308.01054v1 Simulation-based inference using surjective sequential neural likelihood estimation http://arxiv.org/pdf/2307.16695v1 A theory of data variability in Neural Network Bayesian inference http://arxiv.org/pdf/2307.15424v1 Deep Generative Models, Synthetic Tabular Data, and Differential Privacy: An Overview and Synthesis http://arxiv.org/pdf/2307.13813v2 How to Scale Your EMA http://arxiv.org/pdf/2307.12451v1 DiAMoNDBack: Diffusion-denoising Autoregressive Model for Non-Deterministic Backmapping of Cα Protein Traces 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Deep Metric Learning http://arxiv.org/pdf/2306.15337v1 Homological Neural Networks: A Sparse Architecture for Multivariate Complexity http://arxiv.org/pdf/2306.15166v1 Learning from Invalid Data: On Constraint Satisfaction in Generative Models http://arxiv.org/pdf/2306.15159v1 Evaluation of machine learning architectures on the quantification of epistemic and aleatoric uncertainties in complex dynamical systems http://arxiv.org/pdf/2306.15030v1 Equivariant flow matching http://arxiv.org/pdf/2306.14975v1 The Underlying Scaling Laws and Universal Statistical Structure of Complex Datasets http://arxiv.org/pdf/2306.14818v1 Accelerating Molecular Graph Neural Networks via Knowledge Distillation http://arxiv.org/pdf/2306.14624v1 Insights From Insurance for Fair Machine Learning: Responsibility, Performativity and Aggregates http://arxiv.org/pdf/2306.14601v1 Safe Navigation in Unstructured Environments by Minimizing Uncertainty in Control and Perception http://arxiv.org/pdf/2306.14511v1 TaylorPDENet: Learning PDEs from non-grid Data http://arxiv.org/pdf/2306.14510v1 Deep Bayesian Experimental Design for Quantum Many-Body Systems http://arxiv.org/pdf/2306.14476v1 STEF-DHNet: Spatiotemporal External Factors Based Deep Hybrid Network for Enhanced Long-Term Taxi Demand Prediction http://arxiv.org/pdf/2306.14430v1 Enhanced multi-fidelity modelling for digital twin and uncertainty quantification http://arxiv.org/pdf/2306.14258v1 A Neural RDE approach for continuous-time non-Markovian stochastic control problems http://arxiv.org/pdf/2306.14054v1 Towards Understanding Gradient Approximation in Equality Constrained Deep Declarative Networks http://arxiv.org/pdf/2306.14020v1 Individualized Dosing Dynamics via Neural Eigen Decomposition http://arxiv.org/pdf/2306.13957v1 DiffDTM: A conditional structure-free framework for bioactive molecules generation targeted for dual proteins http://arxiv.org/pdf/2306.13931v1 Comparative Study of Predicting Stock Index Using Deep Learning Models http://arxiv.org/pdf/2306.13926v1 Graph Neural Networks Provably Benefit from Structural Information: A Feature Learning Perspective http://arxiv.org/pdf/2306.13867v1 Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems http://arxiv.org/pdf/2306.13830v1 Aircraft Environmental Impact Segmentation via Metric Learning http://arxiv.org/pdf/2306.16133v1 Training Deep Surrogate Models with Large Scale Online Learning http://arxiv.org/pdf/2306.15668v1 Physion++: Evaluating Physical Scene Understanding that Requires Online Inference of Different Physical Properties http://arxiv.org/pdf/2306.14834v1 Scalable Neural Contextual Bandit for Recommender Systems http://arxiv.org/pdf/2306.14545v1 Neural State-Dependent Delay Differential Equations http://arxiv.org/pdf/2306.14356v1 Smart Transformation of EFL Teaching and Learning Approaches http://arxiv.org/pdf/2306.16042v1 Guarantees for data-driven control of nonlinear systems using semidefinite programming: A survey http://arxiv.org/pdf/2306.15530v1 Fast and Automatic 3D Modeling of Antenna Structure Using CNN-LSTM Network for Efficient Data Generation http://arxiv.org/pdf/2306.15381v1 Asymptotic-Preserving Neural Networks for Multiscale Kinetic Equations http://arxiv.org/pdf/2306.15285v1 The $2D$ nonlinear shallow water equations with a partially immersed obstacle http://arxiv.org/pdf/2306.15415v1 Quantum Fourier Networks for Solving Parametric PDEs http://arxiv.org/pdf/2306.14566v1 Estimating Quantum Mutual Information Through a Quantum Neural Network http://arxiv.org/pdf/2306.14184v1 Solution of inverse problem for Gross-Pitaevskii equation with artificial neural networks http://arxiv.org/pdf/2306.15282v2 Variational Latent Discrete Representation for Time Series Modelling http://arxiv.org/pdf/2306.14753v1 The Deep Arbitrary Polynomial Chaos Neural Network or how Deep Artificial Neural Networks could benefit from Data-Driven Homogeneous Chaos Theory ./Link/2023-06-23 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right-hand-sides http://arxiv.org/pdf/2306.13385v1 Solving a class of multi-scale elliptic PDEs by means of Fourier-based mixed physics informed neural networks http://arxiv.org/pdf/2306.13625v1 Fast Macroscopic Forcing Method ./Link/2023-06-22 http://arxiv.org/pdf/2306.12962v1 PyKoopman: A Python Package for Data-Driven Approximation of the Koopman Operator http://arxiv.org/pdf/2306.13030v1 Online Self-Supervised Learning in Machine Learning Intrusion Detection for the Internet of Things http://arxiv.org/pdf/2306.13004v1 Can Differentiable Decision Trees Learn Interpretable Reward Functions? http://arxiv.org/pdf/2306.12900v1 In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD http://arxiv.org/pdf/2306.12818v1 StrainNet: Predicting crystal structure elastic properties using SE(3)-equivariant graph neural networks http://arxiv.org/pdf/2306.12584v1 Hierarchical Neural Simulation-Based Inference Over Event Ensembles http://arxiv.org/pdf/2306.12545v1 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stellar atmospheres with a deep neural network using a 1D convolutional auto encoder for feature extraction http://arxiv.org/pdf/2306.06582v1 Fast, Distribution-free Predictive Inference for Neural Networks with Coverage Guarantees http://arxiv.org/pdf/2306.06281v1 Energy-Dissipative Evolutionary Deep Operator Neural Networks http://arxiv.org/pdf/2306.05674v1 Efficient Uncertainty Quantification and Reduction for Over-Parameterized Neural Networks http://arxiv.org/pdf/2306.05566v1 Data-Adaptive Probabilistic Likelihood Approximation for Ordinary Differential Equations http://arxiv.org/pdf/2306.05415v1 Causal normalizing flows: from theory to practice http://arxiv.org/pdf/2306.04952v1 Entropy-based Training Methods for Scalable Neural Implicit Sampler http://arxiv.org/pdf/2306.04895v1 Solution of physics-based inverse problems using conditional generative adversarial networks with full gradient penalty http://arxiv.org/pdf/2306.04894v1 A Bayesian Framework for learning governing Partial 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estimation with physics informed neural networks http://arxiv.org/pdf/2305.15912v1 Neural Characteristic Activation Value Analysis for Improved ReLU Network Feature Learning http://arxiv.org/pdf/2305.15843v1 TabGSL: Graph Structure Learning for Tabular Data Prediction http://arxiv.org/pdf/2305.15835v1 PDE+: Enhancing Generalization via PDE with Adaptive Distributional Diffusion http://arxiv.org/pdf/2305.15801v1 Lucy-SKG: Learning to Play Rocket League Efficiently Using Deep Reinforcement Learning http://arxiv.org/pdf/2305.15945v1 Learning to Act through Evolution of Neural Diversity in Random Neural Networks http://arxiv.org/pdf/2305.16060v1 Local Randomized Neural Networks with Discontinuous Galerkin Methods for Diffusive-Viscous Wave Equation http://arxiv.org/pdf/2305.15661v1 Accelerated solutions of convection-dominated partial differential equations using implicit feature tracking and empirical quadrature http://arxiv.org/pdf/2305.15976v1 Quantum-Discrete-Map-Based Recurrent Neural Networks ./Link/2023-05-24 http://arxiv.org/pdf/2305.15363v1 Inverse Preference Learning: Preference-based RL without a Reward Function http://arxiv.org/pdf/2305.15234v1 On the road to more accurate mobile cellular traffic predictions http://arxiv.org/pdf/2305.15188v1 Policy Learning based on Deep Koopman Representation http://arxiv.org/pdf/2305.15174v1 Simultaneous identification of models and parameters of scientific simulators http://arxiv.org/pdf/2305.15157v1 Towards More Suitable Personalization in Federated Learning via Decentralized Partial Model Training http://arxiv.org/pdf/2305.14859v1 Utility-Probability Duality of Neural Networks http://arxiv.org/pdf/2305.14765v1 Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference http://arxiv.org/pdf/2305.14644v1 KARNet: Kalman Filter Augmented Recurrent Neural Network for Learning World Models in Autonomous Driving Tasks http://arxiv.org/pdf/2305.14874v1 From Words to Wires: Generating Functioning Electronic Devices from Natural Language Descriptions http://arxiv.org/pdf/2305.14701v1 Modeling rapid language learning by distilling Bayesian priors into artificial neural networks http://arxiv.org/pdf/2305.14799v1 Sample-Efficient Learning for a Surrogate Model of Three-Phase Distribution System http://arxiv.org/pdf/2305.14607v1 An Equivalent Circuit Approach to Distributed Optimization http://arxiv.org/pdf/2305.14703v1 Generative diffusion learning for parametric partial differential equations http://arxiv.org/pdf/2305.15111v1 Reconstruction, forecasting, and stability of chaotic dynamics from partial data http://arxiv.org/pdf/2305.14759v1 Combining direct and indirect sparse data for learning generalizable turbulence models ./Link/2023-05-23 http://arxiv.org/pdf/2305.14286v1 Equivariant Neural Simulators for Stochastic Spatiotemporal Dynamics http://arxiv.org/pdf/2305.14164v1 Improved Convergence of Score-Based Diffusion Models via Prediction-Correction http://arxiv.org/pdf/2305.14077v1 Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension http://arxiv.org/pdf/2305.13664v1 Layer-wise Adaptive Step-Sizes for Stochastic First-Order Methods for Deep Learning http://arxiv.org/pdf/2305.13656v1 Link Prediction without Graph Neural Networks http://arxiv.org/pdf/2305.13588v1 Deep Learning with Kernels through RKHM and the Perron-Frobenius Operator http://arxiv.org/pdf/2305.14022v1 Realistic Noise Synthesis with Diffusion Models http://arxiv.org/pdf/2305.14061v1 An Equivalent Circuit Workflow for Unconstrained Optimization http://arxiv.org/pdf/2305.13879v1 Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis http://arxiv.org/pdf/2305.13341v1 Discovering Causal Relations and Equations from Data http://arxiv.org/pdf/2305.14247v1 Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science 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Inverse Imaging Problems http://arxiv.org/pdf/2305.12817v1 Conservative Physics-Informed Neural Networks for Non-Conservative Hyperbolic Conservation Laws Near Critical States http://arxiv.org/pdf/2305.12625v1 Multirotor Ensemble Model Predictive Control I: Simulation Experiments http://arxiv.org/pdf/2305.12618v1 Atomic and Subgraph-aware Bilateral Aggregation for Molecular Representation Learning http://arxiv.org/pdf/2305.12467v1 Understanding Multi-phase Optimization Dynamics and Rich Nonlinear Behaviors of ReLU Networks http://arxiv.org/pdf/2305.12433v1 ParticleWNN: a Novel Neural Networks Framework for Solving Partial Differential Equations http://arxiv.org/pdf/2305.12396v1 Joint Feature and Differentiable $ k $-NN Graph Learning using Dirichlet Energy http://arxiv.org/pdf/2305.12347v1 Learning Joint 2D & 3D Diffusion Models for Complete Molecule Generation http://arxiv.org/pdf/2305.12334v1 Towards Complex Dynamic Physics System Simulation with Graph Neural ODEs 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infinitesimal density ratio estimation http://arxiv.org/pdf/2305.11778v1 Cross-Lingual Supervision improves Large Language Models Pre-training http://arxiv.org/pdf/2305.11766v1 Transfer operators on graphs: Spectral clustering and beyond http://arxiv.org/pdf/2305.11529v1 A Sequence-to-Sequence Approach for Arabic Pronoun Resolution http://arxiv.org/pdf/2305.11417v1 Complexity of Feed-Forward Neural Networks from the Perspective of Functional Equivalence http://arxiv.org/pdf/2305.11389v1 Domain Generalization Deep Graph Transformation http://arxiv.org/pdf/2305.11863v1 Scaling laws for language encoding models in fMRI http://arxiv.org/pdf/2305.11772v1 Neural Foundations of Mental Simulation: Future Prediction of Latent Representations on Dynamic Scenes http://arxiv.org/pdf/2305.11454v1 An immersed boundary method for the fluid--structure--thermal interaction in rarefied gas flow ./Link/2023-05-18 http://arxiv.org/pdf/2305.11170v1 Efficient Prompting via Dynamic In-Context Learning 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Networks under Weights with Unbounded Variance http://arxiv.org/pdf/2305.10912v1 A Generalist Dynamics Model for Control http://arxiv.org/pdf/2305.10766v1 Adversarial Amendment is the Only Force Capable of Transforming an Enemy into a Friend http://arxiv.org/pdf/2305.10675v1 Tuned Contrastive Learning http://arxiv.org/pdf/2305.10954v1 A Bioinspired Synthetic Nervous System Controller for Pick-and-Place Manipulation ./Link/2023-05-17 http://arxiv.org/pdf/2305.10399v1 End-To-End Latent Variational Diffusion Models for Inverse Problems in High Energy Physics http://arxiv.org/pdf/2305.10309v1 MetaModulation: Learning Variational Feature Hierarchies for Few-Shot Learning with Fewer Tasks http://arxiv.org/pdf/2305.10212v1 A Novel Stochastic LSTM Model Inspired by Quantum Machine Learning http://arxiv.org/pdf/2305.10203v1 Exploring the Space of Key-Value-Query Models with Intention http://arxiv.org/pdf/2305.10157v1 Provably Correct Physics-Informed Neural Networks 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measurements http://arxiv.org/pdf/2305.10423v1 Online data-driven changepoint detection for high-dimensional dynamical systems http://arxiv.org/pdf/2305.10215v1 Modeling long-term large-scale dynamics of turbulence by implicit U-Net enhanced Fourier neural operator http://arxiv.org/pdf/2305.10043v1 Data-driven spectral turbulence modeling for Rayleigh-Bénard convection http://arxiv.org/pdf/2305.09920v1 Low-data deep quantum chemical learning for accurate MP2 and coupled-cluster correlations ./Link/2023-05-16 http://arxiv.org/pdf/2305.09627v1 Addressing computational challenges in physical system simulations with machine learning http://arxiv.org/pdf/2305.09625v1 Conditional variational autoencoder with Gaussian process regression recognition for parametric models http://arxiv.org/pdf/2305.09276v1 Noise robust neural network architecture http://arxiv.org/pdf/2305.09199v1 Machine learning enhanced real-time aerodynamic forces prediction based on sparse pressure sensor inputs 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for Tissue Tracking http://arxiv.org/pdf/2305.07740v1 Double-Iterative Gaussian Process Regression for Modeling Error Compensation in Autonomous Racing http://arxiv.org/pdf/2305.06499v1 State Constrained Stochastic Optimal Control for Continuous and Hybrid Dynamical Systems Using DFBSDE http://arxiv.org/pdf/2305.06920v1 Pseudo-Hamiltonian system identification http://arxiv.org/pdf/2305.04498v3 Leveraging Deep Learning and Digital Twins to Improve Energy Performance of Buildings http://arxiv.org/pdf/2305.03914v1 Variational Nonlinear Kalman Filtering with Unknown Process Noise Covariance http://arxiv.org/pdf/2305.09578v1 Deep Fourier Residual method for solving time-harmonic Maxwell's equations http://arxiv.org/pdf/2305.09408v1 Numerical solution of Poisson partial differential equations in high dimension using deep neural networks http://arxiv.org/pdf/2305.09125v1 A deep learning method for multi-material diffusion problems based on physics-informed neural networks 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through Consensus-based Optimization http://arxiv.org/pdf/2305.02885v1 Input Layer Binarization with Bit-Plane Encoding http://arxiv.org/pdf/2305.02810v1 Interpretable Sentence Representation with Variational Autoencoders and Attention http://arxiv.org/pdf/2305.02776v1 Efficient Personalized Federated Learning via Sparse Model-Adaptation http://arxiv.org/pdf/2305.02657v1 Statistical Optimality of Deep Wide Neural Networks http://arxiv.org/pdf/2305.02507v1 Stimulative Training++: Go Beyond The Performance Limits of Residual Networks http://arxiv.org/pdf/2305.03043v1 Single-Shot Implicit Morphable Faces with Consistent Texture Parameterization http://arxiv.org/pdf/2305.02801v1 A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators http://arxiv.org/pdf/2305.02540v1 Large-Eddy Simulation of Flow over Boeing Gaussian Bump Using Multi-Agent Reinforcement Learning Wall Model http://arxiv.org/pdf/2305.02710v1 Quantum Simulation for Partial Differential Equations with Physical Boundary or Interface Conditions ./Link/2023-05-03 http://arxiv.org/pdf/2305.02122v1 Machine Learning and Structure Formation in Modified Gravity http://arxiv.org/pdf/2305.02019v1 Towards Deep Learning-Based Quantum Algorithms for Solving Nonlinear Partial Differential Equations http://arxiv.org/pdf/2305.02009v1 fairml: A Statistician's Take on Fair Machine Learning Modelling http://arxiv.org/pdf/2305.02299v1 Dynamic Sparse Training with Structured Sparsity http://arxiv.org/pdf/2305.02251v1 Automated Scientific Discovery: From Equation Discovery to Autonomous Discovery Systems http://arxiv.org/pdf/2305.02217v1 Stream Efficient Learning http://arxiv.org/pdf/2305.02009v1 fairml: A Statistician's Take on Fair Machine Learning Modelling http://arxiv.org/pdf/2305.01932v1 Specification-Driven Neural Network Reduction for Scalable Formal Verification http://arxiv.org/pdf/2305.01885v1 Evolving Dictionary Representation for Few-shot Class-incremental Learning http://arxiv.org/pdf/2305.02225v1 Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference http://arxiv.org/pdf/2305.02192v1 Inverse Global Illumination using a Neural Radiometric Prior ./Link/2023-05-02 http://arxiv.org/pdf/2305.00669v1 Reservoir Computing with Error Correction: Long-term Behaviors of Stochastic Dynamical Systems http://arxiv.org/pdf/2305.00351v1 Exploring Optimization Techniques for Parameter Estimation in Nonlinear System Modeling http://arxiv.org/pdf/2305.01539v1 Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows http://arxiv.org/pdf/2305.00214v1 On-off pumping for drag reduction in a turbulent channel flow http://arxiv.org/pdf/2305.01465v1 Efficient estimation of quantum state k-designs with randomized measurements http://arxiv.org/pdf/2305.00997v1 The Expressivity of Classical and Quantum Neural Networks on Entanglement Entropy http://arxiv.org/pdf/2305.00688v1 Expressive Quantum Supervised Machine Learning using Kerr-nonlinear Parametric Oscillators http://arxiv.org/pdf/2305.00653v1 Quantum Solvable Nonlinear Differential Equations http://arxiv.org/pdf/2305.00568v1 Discrete quadratic model QUBO solution landscapes http://arxiv.org/pdf/2305.00378v1 The Open Systems View and the Everett Interpretation http://arxiv.org/pdf/2305.00700v1 Double and Single Descent in Causal Inference with an Application to High-Dimensional Synthetic Control http://arxiv.org/pdf/2305.01604v1 The Training Process of Many Deep Networks Explores the Same Low-Dimensional Manifold http://arxiv.org/pdf/2305.01539v1 Jacobian-Scaled K-means Clustering for Physics-Informed Segmentation of Reacting Flows http://arxiv.org/pdf/2305.01338v1 Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models http://arxiv.org/pdf/2305.01243v1 Machine-Learned Invertible Coarse Graining for Multiscale Molecular Modeling http://arxiv.org/pdf/2305.01166v1 Solving Inverse Problems with Score-Based Generative Priors learned from Noisy Data http://arxiv.org/pdf/2305.01140v1 Geometric Latent Diffusion Models for 3D Molecule Generation ./Link/2023-05-01 http://arxiv.org/pdf/2305.00663v1 Activation Functions Not To Active: A Plausible Theory on Interpreting Neural Networks http://arxiv.org/pdf/2305.00640v1 Inferring the past: a combined CNN-LSTM deep learning framework to fuse satellites for historical inundation mapping http://arxiv.org/pdf/2305.00608v1 Differentiable Neural Networks with RePU Activation: with Applications to Score Estimation and Isotonic Regression http://arxiv.org/pdf/2305.00540v1 SRL-Assisted AFM: Generating Planar Unstructured Quadrilateral Meshes with Supervised and Reinforcement Learning-Assisted Advancing Front Method http://arxiv.org/pdf/2305.00535v1 Nearly Optimal Steiner Trees using Graph Neural Network Assisted Monte Carlo Tree Search http://arxiv.org/pdf/2305.00510v1 Towards Computational Architecture of Liberty: A Comprehensive Survey on Deep Learning for Generating Virtual Architecture in the Metaverse http://arxiv.org/pdf/2305.00478v1 Domain Agnostic Fourier Neural Operators http://arxiv.org/pdf/2305.00362v1 Electricity Price Prediction for Energy Storage System Arbitrage: A Decision-focused Approach http://arxiv.org/pdf/2305.00241v1 When Deep Learning Meets Polyhedral Theory: A Survey http://arxiv.org/pdf/2305.00229v1 Accelerated and Inexpensive Machine Learning for Manufacturing Processes with Incomplete Mechanistic Knowledge http://arxiv.org/pdf/2305.00136v1 Optimizing the AI Development Process by Providing the Best Support Environment http://arxiv.org/pdf/2305.00804v1 Developing Optimization-Based Inverter Models for Short Circuit Studies http://arxiv.org/pdf/2305.00466v1 Efficient and accurate nonlinear model reduction via first-order empirical interpolation http://arxiv.org/pdf/2305.00335v1 Invariant Representations in Deep Learning for Optoacoustic Imaging http://arxiv.org/pdf/2305.01090v1 Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems ./Link/2023-04-28 http://arxiv.org/pdf/2304.14994v1 A Stable and Scalable Method for Solving Initial Value PDEs with Neural Networks http://arxiv.org/pdf/2304.14772v1 Multisample Flow Matching: Straightening Flows with Minibatch Couplings http://arxiv.org/pdf/2304.14766v1 Hyperparameter Optimization through Neural Network Partitioning http://arxiv.org/pdf/2304.14872v1 Parametric model order reduction for a wildland fire model via the shifted POD based deep learning method http://arxiv.org/pdf/2304.14587v1 Smooth Indirect Solution Method for State-constrained Optimal Control Problems with Nonlinear Control-affine Systems http://arxiv.org/pdf/2304.14973v1 Evolutionary Multi-Objective Aerodynamic Design Optimization Using CFD Simulation Incorporating Deep Neural Network http://arxiv.org/pdf/2304.14584v1 Analysing the impact of bottom friction on shallow water waves over idealised bottom topographies http://arxiv.org/pdf/2304.15000v1 Quantum Control Machine: The Limits of Quantum Programs as Data ./Link/2023-04-27 http://arxiv.org/pdf/2304.14214v1 Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information http://arxiv.org/pdf/2304.13900v1 Data-driven Balanced Truncation for Predictive Model Order Reduction of Aeroacoustic Response http://arxiv.org/pdf/2304.13829v1 Controlled density transport using Perron Frobenius generators http://arxiv.org/pdf/2304.13811v1 A Data-Driven Hybrid Automaton Framework to Modeling Complex Dynamical Systems http://arxiv.org/pdf/2304.14374v1 Pseudo-Hamiltonian neural networks for learning partial differential equations http://arxiv.org/pdf/2304.14216v1 Comparison of Stochastic Parametrization Schemes using Data Assimilation on Triad Models http://arxiv.org/pdf/2304.14057v1 Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models http://arxiv.org/pdf/2304.14183v1 A Closed Machine Learning Parametric Reduced Order Model Approach - Application to Turbulent Flows http://arxiv.org/pdf/2304.14118v1 Learning Neural PDE Solvers with Parameter-Guided Channel Attention http://arxiv.org/pdf/2304.14374v1 Pseudo-Hamiltonian neural networks for learning partial differential equations http://arxiv.org/pdf/2304.14369v1 Learning Neural Constitutive Laws From Motion Observations for Generalizable PDE Dynamics http://arxiv.org/pdf/2304.14343v1 Towards Efficient and Comprehensive Urban Spatial-Temporal Prediction: A Unified Library and Performance Benchmark http://arxiv.org/pdf/2304.14214v1 Some of the variables, some of the parameters, some of the times, with some physics known: Identification with partial information http://arxiv.org/pdf/2304.14118v1 Learning Neural PDE Solvers with Parameter-Guided Channel Attention http://arxiv.org/pdf/2304.14057v1 Propagating Kernel Ambiguity Sets in Nonlinear Data-driven Dynamics Models ./Link/2023-04-26 http://arxiv.org/pdf/2304.13039v1 Optimizing Deep Learning Models For Raspberry Pi http://arxiv.org/pdf/2304.13221v1 The Nonlocal Neural Operator: Universal Approximation http://arxiv.org/pdf/2304.13205v1 Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models http://arxiv.org/pdf/2304.13235v1 Anyon Quantum Dimensions from an Arbitrary Ground State Wave Function http://arxiv.org/pdf/2304.13534v1 A mean-field games laboratory for generative modeling http://arxiv.org/pdf/2304.13539v1 Tensor Decomposition for Model Reduction in Neural Networks: A Review http://arxiv.org/pdf/2304.13534v2 A mean-field games laboratory for generative modeling http://arxiv.org/pdf/2304.13469v1 Unsupervised classification of fully kinetic simulations of plasmoid instability using Self-Organizing Maps (SOMs) http://arxiv.org/pdf/2304.13224v1 Score-based Generative Modeling Through Backward Stochastic Differential Equations: Inversion and Generation http://arxiv.org/pdf/2304.13221v1 The Nonlocal Neural Operator: Universal Approximation http://arxiv.org/pdf/2304.13205v1 Splitting physics-informed neural networks for inferring the dynamics of integer- and fractional-order neuron models http://arxiv.org/pdf/2304.13880v1 Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review ./Link/2023-04-25 http://arxiv.org/pdf/2304.11698v1 Hydrodynamic limits for conservative kinetic equations: a spectral and unified approach in the presence of a spectral gap http://arxiv.org/pdf/2304.12586v1 Physics-Informed Representation Learning for Emergent Organization in Complex Dynamical Systems http://arxiv.org/pdf/2304.12865v1 Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks http://arxiv.org/pdf/2304.12541v1 Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems http://arxiv.org/pdf/2304.12598v1 Reconstruction and fast prediction of a 3D flow field based on a variational autoencoder http://arxiv.org/pdf/2304.12420v1 Sample-Efficient and Surrogate-Based Design Optimization of Underwater Vehicle Hulls http://arxiv.org/pdf/2304.12307v1 Optimization of chemical mixers design via tensor trains and quantum computing http://arxiv.org/pdf/2304.11730v1 A fluid flow model for the pressure loss through perforated plates http://arxiv.org/pdf/2304.11347v1 Enhancing the SST Turbulence Model with Symbolic Regression: A Generalizable and Interpretable Data-Driven Approach http://arxiv.org/pdf/2304.11317v1 Forecasting small scale dynamics of fluid turbulence using deep neural networks http://arxiv.org/pdf/2304.12866v1 Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy http://arxiv.org/pdf/2304.12330v1 Parallel bootstrap-based on-policy deep reinforcement learning for continuous flow control applications http://arxiv.org/pdf/2304.12923v1 Quantum Gaussian Process Regression for Bayesian Optimization http://arxiv.org/pdf/2304.12501v1 The cross-sectional stock return predictions via quantum neural network and tensor network http://arxiv.org/pdf/2304.12166v1 Automatic pulse-level calibration by tracking observables using iterative learning http://arxiv.org/pdf/2304.12010v1 Unified Quantum State Tomography and Hamiltonian Learning Using Transformer Models: A Language-Translation-Like Approach for Quantum Systems http://arxiv.org/pdf/2304.12895v1 Discovering Graph Generation Algorithms http://arxiv.org/pdf/2304.12866v1 Binary stochasticity enabled highly efficient neuromorphic deep learning achieves better-than-software accuracy http://arxiv.org/pdf/2304.12747v1 Deep learning based Auto Tuning for Database Management System http://arxiv.org/pdf/2304.12707v2 Learning Robust Deep Equilibrium Models http://arxiv.org/pdf/2304.12586v1 Physics-Informed Representation Learning for Emergent Organization in Complex Dynamical Systems http://arxiv.org/pdf/2304.12579v1 Learning Trajectories are Generalization Indicators http://arxiv.org/pdf/2304.12550v1 Combining Adversaries with Anti-adversaries in Training http://arxiv.org/pdf/2304.12541v2 Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems http://arxiv.org/pdf/2304.12501v1 The cross-sectional stock return predictions via quantum neural network and tensor network http://arxiv.org/pdf/2304.12944v1 Latent Traversals in Generative Models as Potential Flows ./Link/2023-04-24 http://arxiv.org/pdf/2304.12130v1 Reconstructing Turbulent Flows Using Physics-Aware Spatio-Temporal Dynamics and Test-Time Refinement http://arxiv.org/pdf/2304.11758v1 Improving Classification Neural Networks by using Absolute activation function (MNIST/LeNET-5 example) http://arxiv.org/pdf/2304.11732v1 Quantile Extreme Gradient Boosting for Uncertainty Quantification http://arxiv.org/pdf/2304.11702v1 Controlled physics-informed data generation for deep learning-based remaining useful life prediction under unseen operation conditions http://arxiv.org/pdf/2304.11692v1 The Disharmony Between BN and ReLU Causes Gradient Explosion, but is Offset by the Correlation Between Activations http://arxiv.org/pdf/2304.11674v1 A Lightweight Recurrent Learning Network for Sustainable Compressed Sensing http://arxiv.org/pdf/2304.11519v1 Hierarchical Weight Averaging for Deep Neural Networks http://arxiv.org/pdf/2304.11511v1 QuMoS: A Framework for Preserving Security of Quantum Machine Learning Model http://arxiv.org/pdf/2304.11488v1 Physics-guided generative adversarial network to learn physical models http://arxiv.org/pdf/2304.12090v1 Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey http://arxiv.org/pdf/2304.11905v1 Data-driven Knowledge Fusion for Deep Multi-instance Learning http://arxiv.org/pdf/2304.11526v1 How to Control Hydrodynamic Force on Fluidic Pinball via Deep Reinforcement Learning http://arxiv.org/pdf/2304.11470v1 3D-IntPhys: Towards More Generalized 3D-grounded Visual Intuitive Physics under Challenging Scenes http://arxiv.org/pdf/2304.11963v1 Optimal Design of Neural Network Structure for Power System Frequency Security Constraints http://arxiv.org/pdf/2304.11418v1 AC Power Flow Feasibility Restoration via a State Estimation-Based Post-Processing Algorithm http://arxiv.org/pdf/2304.12032v2 lifex-cfd: an open-source computational fluid dynamics solver for cardiovascular applications ./Link/2023-04-21 http://arxiv.org/pdf/2304.10867v1 Application of quantum-inspired generative models to small molecular datasets http://arxiv.org/pdf/2304.10717v1 Physics-informed Neural Network Combined with Characteristic-Based Split for Solving Navier-Stokes Equations http://arxiv.org/pdf/2304.10701v1 Matching-based Data Valuation for Generative Model http://arxiv.org/pdf/2304.10749v1 Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks http://arxiv.org/pdf/2304.10736v1 Generate your neural signals from mine: individual-to-individual EEG converters http://arxiv.org/pdf/2304.10720v1 Conservative Sparse Neural Network Embedded Frequency-Constrained Unit Commitment With Distributed Energy Resources http://arxiv.org/pdf/2304.10842v2 Residual-Based Multi-peak Sampling Algorithm in Inverse Problems of Dynamical Systems http://arxiv.org/pdf/2304.11019v1 UKRmol-scripts: a Perl-based system for the automated operation of the photoionization and electron/positron scattering suite UKRmol+ ./Link/2023-04-20 http://arxiv.org/pdf/2304.10336v1 Controllable Neural Symbolic Regression http://arxiv.org/pdf/2304.10294v1 OptoGPT: A Foundation Model for Inverse Design in Optical Multilayer Thin Film Structures http://arxiv.org/pdf/2304.10277v1 Robust nonlinear set-point control with reinforcement learning http://arxiv.org/pdf/2304.10276v1 Observer-Feedback-Feedforward Controller Structures in Reinforcement Learning http://arxiv.org/pdf/2304.10251v1 Towards replacing precipitation ensemble predictions systems using machine learning http://arxiv.org/pdf/2304.10242v1 Fourier Neural Operator Surrogate Model to Predict 3D Seismic Waves Propagation http://arxiv.org/pdf/2304.10191v1 Efficient Uncertainty Estimation in Spiking Neural Networks via MC-dropout http://arxiv.org/pdf/2304.10159v1 Deep Reinforcement Learning Using Hybrid Quantum Neural Network http://arxiv.org/pdf/2304.10127v1 Learning Sample Difficulty from Pre-trained Models for Reliable Prediction http://arxiv.org/pdf/2304.10061v1 Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size http://arxiv.org/pdf/2304.10051v1 HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic Services http://arxiv.org/pdf/2304.10521v1 A class of mesh-free algorithms for some problems arising in finance and machine learning http://arxiv.org/pdf/2304.10357v1 Drag, lift and torque correlations for axi-symmetric non-spherical particles in locally non-uniform flows ./Link/2023-04-19 http://arxiv.org/pdf/2304.09718v1 Sample-efficient Model-based Reinforcement Learning for Quantum Control http://arxiv.org/pdf/2304.09431v1 Martingale Posterior Neural Processes http://arxiv.org/pdf/2304.09388v1 An Empirical Study of Leveraging Knowledge Distillation for Compressing Multilingual Neural Machine Translation Models http://arxiv.org/pdf/2304.09371v1 3 Dimensional Dense Reconstruction: A Review of Algorithms and Dataset http://arxiv.org/pdf/2304.09835v1 Towards transparent and robust data-driven wind turbine power curve models http://arxiv.org/pdf/2304.09156v1 Using simulation to design an MPC policy for field navigation using GPS sensing http://arxiv.org/pdf/2304.09418v1 Hidden convexity in the heat, linear transport, and Euler's rigid body equations: A computational approach http://arxiv.org/pdf/2304.09802v1 Generalization and Estimation Error Bounds for Model-based Neural Networks http://arxiv.org/pdf/2304.09835v1 Towards transparent and robust data-driven wind turbine power curve models http://arxiv.org/pdf/2304.09802v1 Generalization and Estimation Error Bounds for Model-based Neural Networks http://arxiv.org/pdf/2304.09718v1 Sample-efficient Model-based Reinforcement Learning for Quantum Control http://arxiv.org/pdf/2304.09515v1 Secure Split Learning against Property Inference, Data Reconstruction, and Feature Space Hijacking Attacks http://arxiv.org/pdf/2304.09431v1 Martingale Posterior Neural Processes http://arxiv.org/pdf/2304.09426v1 Decoupled Training for Long-Tailed Classification With Stochastic Representations http://arxiv.org/pdf/2304.09403v1 Wavelets Beat Monkeys at Adversarial Robustness http://arxiv.org/pdf/2304.09376v1 Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction ./Link/2023-04-18 http://arxiv.org/pdf/2304.09157v1 Neural networks for geospatial data http://arxiv.org/pdf/2304.09070v1 M-ENIAC: A machine learning recreation of the first successful numerical weather forecasts http://arxiv.org/pdf/2304.09047v1 Neural Lumped Parameter Differential Equations with Application in Friction-Stir Processing http://arxiv.org/pdf/2304.09426v1 Decoupled Training for Long-Tailed Classification With Stochastic Representations http://arxiv.org/pdf/2304.09403v1 Wavelets Beat Monkeys at Adversarial Robustness http://arxiv.org/pdf/2304.09376v1 Physical Knowledge Enhanced Deep Neural Network for Sea Surface Temperature Prediction http://arxiv.org/pdf/2304.09276v1 A Neural Lambda Calculus: Neurosymbolic AI meets the foundations of computing and functional programming http://arxiv.org/pdf/2304.09224v1 Quantum machine learning for image classification http://arxiv.org/pdf/2304.09759v1 Amplifying Sine Unit: An Oscillatory Activation Function for Deep Neural Networks to Recover Nonlinear Oscillations Efficiently http://arxiv.org/pdf/2304.09750v1 Application of Tensor Neural Networks to Pricing Bermudan Swaptions ./Link/2023-04-17 http://arxiv.org/pdf/2304.08354v1 Tool Learning with Foundation Models http://arxiv.org/pdf/2304.08324v1 Goal-oriented Uncertainty Quantification for Inverse Problems via Variational Encoder-Decoder Networks http://arxiv.org/pdf/2304.08172v1 Pointwise convergence theorem of generalized mini-batch gradient descent in deep neural network http://arxiv.org/pdf/2304.07993v1 In-Context Operator Learning for Differential Equation Problems http://arxiv.org/pdf/2304.07832v1 Characterizing the load profile in power grids by Koopman mode decomposition of interconnected dynamics http://arxiv.org/pdf/2304.07645v1 Non-Proportional Parametrizations for Stable Hypernetwork Learning http://arxiv.org/pdf/2304.07599v1 Learning in latent spaces improves the predictive accuracy of deep neural operators http://arxiv.org/pdf/2304.07558v1 Icospherical Chemical Objects (ICOs) allow for chemical data augmentation and maintain rotational, translation and permutation invariance http://arxiv.org/pdf/2304.07485v1 Critical Sampling for Robust Evolution Operator Learning of Unknown Dynamical Systems http://arxiv.org/pdf/2304.07445v1 A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next steps http://arxiv.org/pdf/2304.07442v1 Learning To Optimize Quantum Neural Network Without Gradients http://arxiv.org/pdf/2304.07869v2 Neural Machine Translation For Low Resource Languages http://arxiv.org/pdf/2304.08481v1 Neural Map Prior for Autonomous Driving http://arxiv.org/pdf/2304.08342v1 NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems http://arxiv.org/pdf/2304.07947v1 Deep Neural Network Approximation of Composition Functions: with application to PINNs http://arxiv.org/pdf/2304.08301v1 Quantized vortex dynamics of the complex Ginzburg-Landau equation on torus http://arxiv.org/pdf/2304.08066v1 On the benefit of overparameterisation in state reconstruction: An empirical study of the nonlinear case http://arxiv.org/pdf/2304.07556v1 A nonlinear model of opinion dynamics on networks with friction-inspired stubbornness http://arxiv.org/pdf/2304.08457v1 Deep Learning Criminal Networks ./Link/2023-04-14 http://arxiv.org/pdf/2304.07245v1 Machine Learning-Based Multi-Objective Design Exploration Of Flexible Disc Elements http://arxiv.org/pdf/2304.07070v1 Who breaks early, looses: goal oriented training of deep neural networks based on port Hamiltonian dynamics http://arxiv.org/pdf/2304.07029v1 Long-term instabilities of deep learning-based digital twins of the climate system: The cause and a solution http://arxiv.org/pdf/2304.06972v1 Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator http://arxiv.org/pdf/2304.06875v1 Research without Re-search: Maximal Update Parametrization Yields Accurate Loss Prediction across Scales http://arxiv.org/pdf/2304.07143v1 A Review on Longitudinal Car-Following Model http://arxiv.org/pdf/2304.07262v1 Phantom Embeddings: Using Embedding Space for Model Regularization in Deep Neural Networks http://arxiv.org/pdf/2304.07139v1 Neuromorphic Optical Flow and Real-time Implementation with Event Cameras http://arxiv.org/pdf/2304.06902v1 Quantum Algorithms for Multiscale Partial Differential Equations http://arxiv.org/pdf/2304.07046v1 Data-Driven Modeling for Transonic Aeroelastic Analysis ./Link/2023-04-13 http://arxiv.org/pdf/2304.06349v1 Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification http://arxiv.org/pdf/2304.06058v1 Consistent Point Data Assimilation in Firedrake and Icepack http://arxiv.org/pdf/2304.06681v1 Exploring Quantum Neural Networks for the Discovery and Implementation of Quantum Error-Correcting Codes http://arxiv.org/pdf/2304.06587v1 Anderson impurity solver integrating tensor network methods with quantum computing http://arxiv.org/pdf/2304.06686v1 OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems http://arxiv.org/pdf/2304.06326v1 Understanding Overfitting in Adversarial Training in Kernel Regression http://arxiv.org/pdf/2304.06686v1 OKRidge: Scalable Optimal k-Sparse Ridge Regression for Learning Dynamical Systems http://arxiv.org/pdf/2304.06349v1 Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification http://arxiv.org/pdf/2304.06326v1 Understanding Overfitting in Adversarial Training in Kernel Regression http://arxiv.org/pdf/2304.06253v1 Enhancing Model Learning and Interpretation Using Multiple Molecular Graph Representations for Compound Property and Activity Prediction http://arxiv.org/pdf/2304.06234v1 Physics-informed radial basis network (PIRBN): A local approximation neural network for solving nonlinear PDEs http://arxiv.org/pdf/2304.06287v1 NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds http://arxiv.org/pdf/2304.06378v1 Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning ./Link/2023-04-12 http://arxiv.org/pdf/2304.05991v1 Maximum-likelihood Estimators in Physics-Informed Neural Networks for High-dimensional Inverse Problems http://arxiv.org/pdf/2304.05980v1 Neural Attention Forests: Transformer-Based Forest Improvement http://arxiv.org/pdf/2304.05790v1 Deep neural network approximation of composite functions without the curse of dimensionality http://arxiv.org/pdf/2304.05592v1 Learned multiphysics inversion with differentiable programming and machine learning http://arxiv.org/pdf/2304.05627v1 Constructing Deep Spiking Neural Networks from Artificial Neural Networks with Knowledge Distillation http://arxiv.org/pdf/2304.05543v1 Group projected Subspace Pursuit for Identification of variable coefficient differential equations (GP-IDENT) http://arxiv.org/pdf/2304.06378v1 Generalizable Deep Learning Method for 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spider web structures using generative deep learning and additive manufacturing http://arxiv.org/pdf/2304.05133v1 Neural Network Architectures http://arxiv.org/pdf/2304.05055v1 A Comprehensive Survey on Deep Graph Representation Learning http://arxiv.org/pdf/2304.04964v2 A priori compression of convolutional neural networks for wave simulators http://arxiv.org/pdf/2304.04906v1 Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing http://arxiv.org/pdf/2304.04985v1 Efficient Feature Description for Small Body Relative Navigation using Binary Convolutional Neural Networks http://arxiv.org/pdf/2304.05138v1 Distributed Event-Triggered Online Learning for Multi-Agent System Control using Gaussian Process Regression http://arxiv.org/pdf/2304.05021v1 Translating Assembly Accuracy Requirements to Cut-Off Frequencies for Component Mode Synthesis ./Link/2023-04-10 http://arxiv.org/pdf/2304.04697v1 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Nonlinear Control via Finite-dimensional Spectral Dynamic Embedding http://arxiv.org/pdf/2304.03894v1 A multifidelity approach to continual learning for physical systems http://arxiv.org/pdf/2304.04746v1 A Cheaper and Better Diffusion Language Model with Soft-Masked Noise http://arxiv.org/pdf/2304.03949v1 Capturing dynamical correlations using implicit neural representations http://arxiv.org/pdf/2304.04278v1 Point-SLAM: Dense Neural Point Cloud-based SLAM http://arxiv.org/pdf/2304.04046v1 Regularised Learning with Selected Physics for Power System Dynamics ./Link/2023-04-07 http://arxiv.org/pdf/2304.03722v1 Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic Data http://arxiv.org/pdf/2304.03589v1 On Efficient Training of Large-Scale Deep Learning Models: A Literature Review http://arxiv.org/pdf/2304.03571v1 $β$-Variational autoencoders and transformers for reduced-order modelling of fluid flows http://arxiv.org/pdf/2304.03552v1 A physics-informed neural network framework for modeling obstacle-related equations http://arxiv.org/pdf/2304.03689v1 EPINN-NSE: Enhanced Physics-Informed Neural Networks for Solving Navier-Stokes Equations http://arxiv.org/pdf/2304.03519v1 Robust data-driven control for nonlinear systems using the Koopman operator http://arxiv.org/pdf/2304.03634v1 Hydrodynamic limit of the multi-component slow boundary WASEP with collisions ./Link/2023-04-06 http://arxiv.org/pdf/2304.02976v1 Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations http://arxiv.org/pdf/2304.02972v1 Training a Two Layer ReLU Network Analytically http://arxiv.org/pdf/2304.02902v1 Towards Efficient MCMC Sampling in Bayesian Neural Networks by Exploiting Symmetry http://arxiv.org/pdf/2304.02811v1 HomPINNs: homotopy physics-informed neural networks for solving the inverse problems of nonlinear differential equations with multiple solutions http://arxiv.org/pdf/2304.03122v1 Is it conceivable that neurogenesis, neural Darwinism, and species evolution could all serve as inspiration for the creation of evolutionary deep neural networks? http://arxiv.org/pdf/2304.02841v1 Learning Neural Eigenfunctions for Unsupervised Semantic Segmentation http://arxiv.org/pdf/2304.02820v1 Analyzing Topological Mixing and Chaos on Continua with Symbolic Dynamics http://arxiv.org/pdf/2304.02966v1 Collective variables between large-scale states in turbulent convection http://arxiv.org/pdf/2304.02865v1 Quantum simulation of discrete linear dynamical systems and simple iterative methods in linear algebra via Schrodingerisation ./Link/2023-04-05 http://arxiv.org/pdf/2304.02637v1 GenPhys: From Physical Processes to Generative Models http://arxiv.org/pdf/2304.02282v1 About optimal loss function for training physics-informed neural networks under respecting causality http://arxiv.org/pdf/2304.02281v1 Multilevel Optimization for Policy Design with Agent-Based Epidemic Models http://arxiv.org/pdf/2304.02601v1 Efficient Gradient-based Optimization for Reconstructing Binary Images in Applications to Electrical Impedance Tomography http://arxiv.org/pdf/2304.02637v1 GenPhys: From Physical Processes to Generative Models http://arxiv.org/pdf/2304.02381v1 Physics-Inspired Interpretability Of Machine Learning Models ./Link/2023-04-04 http://arxiv.org/pdf/2304.01963v1 Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography http://arxiv.org/pdf/2304.01658v1 Fully Convolutional Networks for Dense Water Flow Intensity Prediction in Swedish Catchment Areas http://arxiv.org/pdf/2304.01565v1 A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material http://arxiv.org/pdf/2304.01042v1 DivClust: Controlling Diversity in Deep Clustering http://arxiv.org/pdf/2304.00948v1 VTAE: Variational Transformer Autoencoder with Manifolds Learning http://arxiv.org/pdf/2304.01395v1 Learning Personalized Models with Clustered System Identification http://arxiv.org/pdf/2304.00720v1 Data-Driven Track Following Control for Dual Stage-Actuator Hard Disk Drives http://arxiv.org/pdf/2304.00352v1 Universal approximation of flows of control systems by recurrent neural networks http://arxiv.org/pdf/2304.01294v1 Sparse Cholesky Factorization for Solving Nonlinear PDEs via Gaussian Processes http://arxiv.org/pdf/2304.01418v1 Generalized Data-driven Predictive Control http://arxiv.org/pdf/2304.01329v1 Learning the Delay Using Neural Delay Differential Equations http://arxiv.org/pdf/2304.01782v1 Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximation http://arxiv.org/pdf/2304.01732v1 Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems http://arxiv.org/pdf/2304.01996v1 Autoregressive Neural TensorNet: Bridging Neural Networks and Tensor Networks for Quantum Many-Body Simulation http://arxiv.org/pdf/2304.01325v1 Deep learning neural network for approaching Schrödinger problems with arbitrary two-dimensional confinement http://arxiv.org/pdf/2304.01762v1 Incorporating Unlabelled Data into Bayesian Neural Networks http://arxiv.org/pdf/2304.01561v1 Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression http://arxiv.org/pdf/2304.01996v1 Autoregressive Neural TensorNet: Bridging Neural Networks and Tensor Networks for Quantum Many-Body Simulation http://arxiv.org/pdf/2304.02381v1 Physics-Inspired Interpretability Of Machine Learning Models http://arxiv.org/pdf/2304.02119v1 Initialization Approach for Nonlinear State-Space Identification via the Subspace Encoder Approach http://arxiv.org/pdf/2304.02096v1 The CAMELS project: Expanding the galaxy formation model space with new ASTRID and 28-parameter TNG and SIMBA suites http://arxiv.org/pdf/2304.01963v1 Model-corrected learned primal-dual models for fast limited-view photoacoustic tomography http://arxiv.org/pdf/2304.01762v1 Incorporating Unlabelled Data into Bayesian Neural Networks http://arxiv.org/pdf/2304.02480v1 Quantum Imitation Learning http://arxiv.org/pdf/2304.02472v1 Short-Term Volatility Prediction Using Deep CNNs Trained on Order Flow http://arxiv.org/pdf/2304.01732v1 Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems http://arxiv.org/pdf/2304.01565v1 A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material http://arxiv.org/pdf/2304.01561v1 Optimal rates of approximation by shallow ReLU$^k$ neural networks and applications to nonparametric regression ./Link/2023-04-03 http://arxiv.org/pdf/2304.00970v1 Development and Evaluation of Conformal Prediction Methods for QSAR http://arxiv.org/pdf/2304.00909v1 Laplace-fPINNs: Laplace-based fractional physics-informed neural networks for solving forward and inverse problems of subdiffusion http://arxiv.org/pdf/2304.00738v1 Device Image-IV Mapping using Variational Autoencoder for Inverse Design and Forward Prediction http://arxiv.org/pdf/2304.00732v1 Leveraging Predictive Models for Adaptive Sampling of Spatiotemporal Fluid Processes http://arxiv.org/pdf/2304.00575v1 Modelling customer churn for the retail industry in a deep learning based sequential framework http://arxiv.org/pdf/2304.00549v1 Variational Denoising for Variational Quantum Eigensolver http://arxiv.org/pdf/2304.00486v1 Learning by Grouping: A Multilevel Optimization Framework for Improving Fairness in Classification without Losing Accuracy http://arxiv.org/pdf/2304.00388v2 Multilevel CNNs for Parametric PDEs http://arxiv.org/pdf/2304.00369v1 Physics-informed machine learning for moving load problems http://arxiv.org/pdf/2304.00320v1 Doubly Stochastic Models: Learning with Unbiased Label Noises and Inference Stability ./Link/2023-03-31 http://arxiv.org/pdf/2303.18087v1 Evaluation Challenges for Geospatial ML http://arxiv.org/pdf/2303.18017v1 Rapid prediction of lab-grown tissue properties using deep learning http://arxiv.org/pdf/2303.17963v1 Learning-Based Optimal Control with Performance Guarantees for Unknown Systems with Latent States http://arxiv.org/pdf/2303.17934v1 Conflict-Averse Gradient Optimization of Ensembles for Effective Offline Model-Based Optimization http://arxiv.org/pdf/2303.17879v1 CoSMo: a Framework for Implementing Conditioned Process Simulation Models http://arxiv.org/pdf/2303.17764v1 Towards Adversarially Robust Continual Learning http://arxiv.org/pdf/2303.17657v1 Progress towards an improved particle flow algorithm at CMS with machine learning http://arxiv.org/pdf/2303.18095v1 Quantum computing quantum Monte Carlo with hybrid tensor network toward electronic structure calculations of large-scale molecular and solid systems http://arxiv.org/pdf/2303.17893v1 Improved clinical data imputation via classical and quantum determinantal point processes ./Link/2023-03-30 http://arxiv.org/pdf/2303.17468v1 Surrogate Neural Networks for Efficient Simulation-based Trajectory Planning Optimization http://arxiv.org/pdf/2303.17078v1 Machine Learning for Partial Differential Equations http://arxiv.org/pdf/2303.17305v1 Computationally efficient predictive control based on ANN state-space model http://arxiv.org/pdf/2303.17178v1 Data-driven approach for modelling Reynolds stress tensor with invariance preservation http://arxiv.org/pdf/2303.17079v1 Inverse-designed Silicon Carbide Quantum and Nonlinear Photonics ./Link/2023-03-29 http://arxiv.org/pdf/2303.16897v1 Physics-Driven Diffusion Models for Impact Sound Synthesis from Videos http://arxiv.org/pdf/2303.16866v1 ALUM: Adversarial Data Uncertainty Modeling from Latent Model Uncertainty Compensation http://arxiv.org/pdf/2303.16725v1 Machine Learning for Uncovering Biological Insights in Spatial Transcriptomics Data http://arxiv.org/pdf/2303.16674v1 Neuro-symbolic Rule Learning in Real-world Classification Tasks http://arxiv.org/pdf/2303.16656v1 Learning Flow Functions from Data with Applications to Nonlinear Oscillators http://arxiv.org/pdf/2303.16589v1 Poster: Link between Bias, Node Sensitivity and Long-Tail Distribution in trained DNNs http://arxiv.org/pdf/2303.16585v1 Quantum Deep Hedging http://arxiv.org/pdf/2303.16464v1 Lipschitzness Effect of a Loss Function on Generalization Performance of Deep Neural Networks Trained by Adam and AdamW Optimizers http://arxiv.org/pdf/2303.16454v1 Conductivity Imaging from Internal Measurements with Mixed Least-Squares Deep Neural Networks http://arxiv.org/pdf/2303.16424v1 ProductAE: Toward Deep Learning Driven Error-Correction Codes of Large Dimensions http://arxiv.org/pdf/2303.16412v1 A Comprehensive and Versatile Multimodal Deep Learning Approach for Predicting Diverse Properties of Advanced Materials http://arxiv.org/pdf/2303.16550v1 Potential quantum advantage for simulation of fluid dynamics http://arxiv.org/pdf/2303.16449v1 A Tutorial on Quantum Master Equations: Tips and tricks for quantum optics, quantum computing and beyond ./Link/2023-03-28 http://arxiv.org/pdf/2303.14564v1 Compositional Neural Certificates for Networked Dynamical Systems http://arxiv.org/pdf/2303.16110v1 Invariant preservation in machine learned PDE solvers via error correction http://arxiv.org/pdf/2303.15827v1 PDExplain: Contextual Modeling of PDEs in the Wild http://arxiv.org/pdf/2303.15704v1 Adaptive trajectories sampling for solving PDEs with deep learning methods http://arxiv.org/pdf/2303.14432v1 Weighted reduced order methods for uncertainty quantification in computational fluid dynamics http://arxiv.org/pdf/2303.14858v1 Nonlinear inviscid damping for 2-D inhomogeneous incompressible Euler equations http://arxiv.org/pdf/2303.14477v1 A primer on quasi-convex functions in nonlinear potential theories http://arxiv.org/pdf/2303.15779v1 Learnability of Linear Port-Hamiltonian Systems http://arxiv.org/pdf/2303.15634v1 Learning Rate Schedules in the Presence of Distribution Shift http://arxiv.org/pdf/2303.15832v1 The transformative potential of machine learning for experiments in fluid mechanics http://arxiv.org/pdf/2303.15626v1 A Framework for Demonstrating Practical Quantum Advantage: Racing Quantum against Classical Generative Models http://arxiv.org/pdf/2303.15739v1 Bayesian Free Energy of Deep ReLU Neural Network in Overparametrized Cases http://arxiv.org/pdf/2303.16110v2 Invariant preservation in machine learned PDE solvers via error correction http://arxiv.org/pdf/2303.15973v1 Do Neural Topic Models Really Need Dropout? Analysis of the Effect of Dropout in Topic Modeling http://arxiv.org/pdf/2303.15849v1 GAS: A Gaussian Mixture Distribution-Based Adaptive Sampling Method for PINNs http://arxiv.org/pdf/2303.15834v1 Enabling Inter-organizational Analytics in Business Networks Through Meta Machine Learning http://arxiv.org/pdf/2303.15833v1 Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning http://arxiv.org/pdf/2303.15827v1 PDExplain: Contextual Modeling of PDEs in the Wild http://arxiv.org/pdf/2303.15739v1 Bayesian Free Energy of Deep ReLU Neural Network in Overparametrized Cases http://arxiv.org/pdf/2303.15681v1 GNN-based physics solver for time-independent PDEs http://arxiv.org/pdf/2303.15832v2 The transformative potential of machine learning for experiments in fluid mechanics ./Link/2023-03-27 http://arxiv.org/pdf/2303.15350v1 Improving Neural Topic Models with Wasserstein Knowledge Distillation http://arxiv.org/pdf/2303.15201v1 An active inference model of car following: Advantages and applications http://arxiv.org/pdf/2303.15057v1 Meta-Calibration Regularized Neural Networks http://arxiv.org/pdf/2303.15053v1 Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC http://arxiv.org/pdf/2303.15041v1 Towards black-box parameter estimation http://arxiv.org/pdf/2303.14878v1 GPT-PINN: Generative Pre-Trained Physics-Informed Neural Networks toward non-intrusive Meta-learning of parametric PDEs http://arxiv.org/pdf/2303.14844v1 Analyzing Convergence in Quantum Neural Networks: Deviations from Neural Tangent Kernels http://arxiv.org/pdf/2303.14745v1 Combining General and Personalized Models for Epilepsy Detection with Hyperdimensional Computing http://arxiv.org/pdf/2303.14554v1 Deep Kernel Methods Learn Better: From Cards to Process Optimization http://arxiv.org/pdf/2303.14537v1 Deep Augmentation: Enhancing Self-Supervised Learning through Transformations in Higher Activation Space http://arxiv.org/pdf/2303.14519v1 Stochastic Model Predictive Control Utilizing Bayesian Neural Networks http://arxiv.org/pdf/2303.14496v1 Learning with Explanation Constraints http://arxiv.org/pdf/2303.14483v1 Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey http://arxiv.org/pdf/2303.14468v1 Autoregressive Conditional Neural Processes http://arxiv.org/pdf/2303.15833v1 Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning http://arxiv.org/pdf/2303.15245v1 Comparison between layer-to-layer network training and conventional network training using Convolutional Neural Networks http://arxiv.org/pdf/2303.15101v2 DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering http://arxiv.org/pdf/2303.14845v1 Multi-task Learning of Histology and Molecular Markers for Classifying Diffuse Glioma http://arxiv.org/pdf/2303.15487v1 Knowledge Enhanced Graph Neural Networks ./Link/2023-03-24 http://arxiv.org/pdf/2303.14186v1 TRAK: Attributing Model Behavior at Scale http://arxiv.org/pdf/2303.14116v1 Improving Prediction Performance and Model Interpretability through Attention Mechanisms from Basic and Applied Research Perspectives http://arxiv.org/pdf/2303.14090v1 Physics-informed neural networks in the recreation of hydrodynamic simulations from dark matter http://arxiv.org/pdf/2303.14083v1 Online Learning for the Random Feature Model in the Student-Teacher Framework http://arxiv.org/pdf/2303.14006v1 ASTRA-sim2.0: Modeling Hierarchical Networks and Disaggregated Systems for Large-model Training at Scale http://arxiv.org/pdf/2303.13915v1 Benchmarking the Impact of Noise on Deep Learning-based Classification of Atrial Fibrillation in 12-Lead ECG http://arxiv.org/pdf/2303.13773v1 A Graph Neural Network Approach to Nanosatellite Task Scheduling: Insights into Learning Mixed-Integer Models http://arxiv.org/pdf/2303.13752v1 Leveraging Old Knowledge to Continually Learn New Classes in Medical Images http://arxiv.org/pdf/2303.13746v1 FixFit: using parameter-compression to solve the inverse problem in overdetermined models http://arxiv.org/pdf/2303.14001v1 Grid-guided Neural Radiance Fields for Large Urban Scenes http://arxiv.org/pdf/2303.13826v1 Hard Sample Matters a Lot in Zero-Shot Quantization ./Link/2023-03-23 http://arxiv.org/pdf/2303.13462v1 Generalization with quantum geometry for learning unitaries http://arxiv.org/pdf/2303.13117v1 RLOR: A Flexible Framework of Deep Reinforcement Learning for Operation Research http://arxiv.org/pdf/2303.13093v1 The Probabilistic Stability of Stochastic Gradient Descent http://arxiv.org/pdf/2303.13056v1 Predicting the Initial Conditions of the Universe using Deep Learning http://arxiv.org/pdf/2303.13055v1 Reimagining Application User Interface (UI) Design using Deep Learning Methods: Challenges and Opportunities http://arxiv.org/pdf/2303.13022v1 ENVIDR: Implicit Differentiable Renderer with Neural Environment Lighting http://arxiv.org/pdf/2303.12992v1 A Survey of Historical Learning: Learning Models with Learning History http://arxiv.org/pdf/2303.13196v1 Modeling Minimum Cost Network Flows With Port-Hamiltonian Systems ./Link/2023-03-22 http://arxiv.org/pdf/2303.12703v1 Causal Reasoning in the Presence of Latent Confounders via Neural ADMG Learning http://arxiv.org/pdf/2303.12643v1 Traffic Volume Prediction using Memory-Based Recurrent Neural Networks: A comparative analysis of LSTM and GRU http://arxiv.org/pdf/2303.12524v1 Split-Et-Impera: A Framework for the Design of Distributed Deep Learning Applications http://arxiv.org/pdf/2303.12261v1 Challenges and opportunities for machine learning in multiscale computational modeling http://arxiv.org/pdf/2303.12245v1 Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time 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http://arxiv.org/pdf/2303.10825v1 TenCirChem: An Efficient Quantum Computational Chemistry Package for the NISQ Era http://arxiv.org/pdf/2303.11833v1 Materials Discovery with Extreme Properties via AI-Driven Combinatorial Chemistry http://arxiv.org/pdf/2303.11756v1 Improving Deep Dynamics Models for Autonomous Vehicles with Multimodal Latent Mapping of Surfaces http://arxiv.org/pdf/2303.11735v1 Tensor networks for quantum machine learning http://arxiv.org/pdf/2303.11577v2 Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations ./Link/2023-03-20 http://arxiv.org/pdf/2303.11249v1 What Makes Data Suitable for a Locally Connected Neural Network? 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Structure Calculation via a Quantum Computer http://arxiv.org/pdf/2303.09788v1 Higher-order quantum transformations of Hamiltonian dynamics ./Link/2023-03-16 http://arxiv.org/pdf/2303.09491v1 Challenges and Opportunities in Quantum Machine Learning http://arxiv.org/pdf/2303.09273v1 Adaptive Modeling of Uncertainties for Traffic Forecasting http://arxiv.org/pdf/2303.09154v1 Bayesian Generalization Error in Linear Neural Networks with Concept Bottleneck Structure and Multitask Formulation http://arxiv.org/pdf/2303.09343v1 Real-time elastic partial shape matching using a neural network-based adjoint method http://arxiv.org/pdf/2303.09056v1 Generating synthetic multi-dimensional molecular-mediator time series data for artificial intelligence-based disease trajectory forecasting and drug development digital twins: Considerations http://arxiv.org/pdf/2303.09012v1 Exploring the Power of Generative Deep Learning for Image-to-Image Translation and MRI Reconstruction: A Cross-Domain Review 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http://arxiv.org/pdf/2303.08797v1 Stochastic Interpolants: A Unifying Framework for Flows and Diffusions http://arxiv.org/pdf/2303.08690v1 Replay Buffer With Local Forgetting for Adaptive Deep Model-Based Reinforcement Learning http://arxiv.org/pdf/2303.08291v1 Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability ./Link/2023-03-14 http://arxiv.org/pdf/2303.07994v1 Learning for Precision Motion of an Interventional X-ray System: Add-on Physics-Guided Neural Network Feedforward Control http://arxiv.org/pdf/2303.07912v1 Error estimates of deep learning methods for the nonstationary Magneto-hydrodynamics equations http://arxiv.org/pdf/2303.07261v1 Ab initio electron-lattice downfolding: potential energy landscapes, anharmonicity, and molecular dynamics in charge density wave materials http://arxiv.org/pdf/2303.08081v1 Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions http://arxiv.org/pdf/2303.07392v1 Efficient Bayesian Physics Informed Neural Networks for Inverse Problems via Ensemble Kalman Inversion http://arxiv.org/pdf/2303.08081v1 Explanation Shift: Investigating Interactions between Models and Shifting Data Distributions http://arxiv.org/pdf/2303.08054v2 Statistical Hardware Design With Multi-model Active Learning http://arxiv.org/pdf/2303.08291v1 Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability http://arxiv.org/pdf/2303.08272v1 Automated patent extraction powers generative modeling in focused chemical spaces http://arxiv.org/pdf/2303.08227v1 Hall effect thruster design via deep neural network for additive manufacturing http://arxiv.org/pdf/2303.08187v1 Vehicle lateral control using Machine Learning for automated vehicle guidance http://arxiv.org/pdf/2303.07925v1 Understanding Model Complexity for temporal tabular and multi-variate time series, case study with Numerai data science tournament http://arxiv.org/pdf/2303.07735v1 Can neural networks do arithmetic? A survey on the elementary numerical skills of state-of-the-art deep learning models http://arxiv.org/pdf/2303.07647v2 Recent Advances and Applications of Machine Learning in Experimental Solid Mechanics: A Review http://arxiv.org/pdf/2303.07546v1 Constrained Adversarial Learning and its applicability to Automated Software Testing: a systematic review ./Link/2023-03-13 http://arxiv.org/pdf/2303.07310v1 Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks http://arxiv.org/pdf/2303.07153v1 SA-CNN: Application to text categorization issues using simulated annealing-based convolutional neural network optimization http://arxiv.org/pdf/2303.07127v2 Improving physics-informed neural networks with meta-learned optimization http://arxiv.org/pdf/2303.07009v1 Symbolic Regression for PDEs using Pruned Differentiable Programs http://arxiv.org/pdf/2303.06972v1 Leveraging Neural Koopman Operators to Learn Continuous Representations of Dynamical Systems from Scarce Data http://arxiv.org/pdf/2303.06902v1 Molecular Property Prediction by Semantic-invariant Contrastive Learning http://arxiv.org/pdf/2303.06871v2 Physics-driven machine learning models coupling PyTorch and Firedrake http://arxiv.org/pdf/2303.06572v1 Predictive Experience Replay for Continual Visual Control and Forecasting http://arxiv.org/pdf/2303.06561v1 Phase Diagram of Initial Condensation for Two-layer Neural Networks http://arxiv.org/pdf/2303.06515v1 Multistage Stochastic Optimization via Kernels http://arxiv.org/pdf/2303.06471v1 Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review http://arxiv.org/pdf/2303.06455v1 Graph Neural Network contextual embedding for Deep Learning on Tabular Data http://arxiv.org/pdf/2303.06381v1 Learning to Precode for Integrated Sensing and Communications Systems http://arxiv.org/pdf/2303.06289v1 Machine Learning Enhanced Hankel Dynamic-Mode Decomposition http://arxiv.org/pdf/2303.06263v1 Quantum Machine Learning Implementations: Proposals and Experiments http://arxiv.org/pdf/2303.07176v1 Reduced order model of a convection-diffusion equation using Proper Orthogonal Decomposition ./Link/2023-03-10 http://arxiv.org/pdf/2303.05860v1 Variational Quantum Neural Networks (VQNNS) in Image Classification http://arxiv.org/pdf/2303.05796v1 Training, Architecture, and Prior for Deterministic Uncertainty Methods http://arxiv.org/pdf/2303.05728v1 On the effectiveness of neural priors in modeling dynamical systems http://arxiv.org/pdf/2303.05718v1 Tradeoff of generalization error in unsupervised learning http://arxiv.org/pdf/2303.05698v1 Fairness-enhancing deep learning for ride-hailing demand prediction http://arxiv.org/pdf/2303.05641v1 Efficient Real Time Recurrent Learning through combined activity and parameter sparsity http://arxiv.org/pdf/2303.06138v1 Learning Object-Centric Neural Scattering Functions for Free-viewpoint Relighting and Scene Composition http://arxiv.org/pdf/2303.06130v1 Full State Estimation of Soft Robots From Tip Velocities: A Cosserat-Theoretic Boundary Observer ./Link/2023-03-09 http://arxiv.org/pdf/2303.05512v1 PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification http://arxiv.org/pdf/2303.05506v1 TANGOS: Regularizing Tabular Neural Networks through Gradient Orthogonalization and Specialization http://arxiv.org/pdf/2303.05231v1 Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework http://arxiv.org/pdf/2303.05151v1 Provable Data Subset Selection For Efficient Neural Network Training http://arxiv.org/pdf/2303.05323v1 Controllable Video Generation by Learning the Underlying Dynamical System with Neural ODE http://arxiv.org/pdf/2303.05262v1 Fredholm integral equations for function approximation and the training of neural networks http://arxiv.org/pdf/2303.05386v1 Deep Equilibrium Learning of Explicit Regularizers for Imaging Inverse Problems ./Link/2023-03-08 http://arxiv.org/pdf/2303.04778v1 Fourier-MIONet: Fourier-enhanced multiple-input neural operators for multiphase modeling of geological carbon sequestration http://arxiv.org/pdf/2303.04777v1 LMI-based Data-Driven Robust Model Predictive Control http://arxiv.org/pdf/2303.04759v1 RAF: Holistic Compilation for Deep Learning Model Training http://arxiv.org/pdf/2303.04436v1 A comparison of rational and neural network based approximations http://arxiv.org/pdf/2303.04391v1 A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces http://arxiv.org/pdf/2303.04339v1 Learning the Finer Things: Bayesian Structure Learning at the Instantiation Level http://arxiv.org/pdf/2303.04496v1 MenuCraft: Interactive Menu System Design with Large Language Models http://arxiv.org/pdf/2303.04341v1 Neural Vector Fields: Implicit Representation by Explicit Learning http://arxiv.org/pdf/2303.04679v1 Flow reconstruction by multiresolution optimization of a discrete loss with automatic differentiation ./Link/2023-03-07 http://arxiv.org/pdf/2303.04145v1 Benign Overfitting for Two-layer ReLU Networks http://arxiv.org/pdf/2303.04143v1 Can We Scale Transformers to Predict Parameters of Diverse ImageNet Models? http://arxiv.org/pdf/2303.04129v1 Foundation Models for Decision Making: Problems, Methods, and Opportunities http://arxiv.org/pdf/2303.03894v1 Manually Selecting The Data Function for Supervised Learning of small datasets http://arxiv.org/pdf/2303.03769v1 Learning Hamiltonian Systems with Mono-Implicit Runge-Kutta Methods http://arxiv.org/pdf/2303.03707v1 Hybrid quantum-classical convolutional neural network for phytoplankton classification http://arxiv.org/pdf/2303.04035v1 Data Assimilation for Combined Parameter and State Estimation in Stochastic Continuous-Discrete Nonlinear Systems http://arxiv.org/pdf/2303.03848v1 Parareal with a physics-informed neural network as coarse propagator http://arxiv.org/pdf/2303.04102v1 Lyapunov exponents and invariant manifolds for linear stochastic partial functional differential equations http://arxiv.org/pdf/2303.03806v1 Operator learning of RANS equations: a Graph Neural Network closure model http://arxiv.org/pdf/2303.03695v1 Prediction of transonic flow over supercritical airfoils using geometric-encoding and deep-learning strategies ./Link/2023-03-06 http://arxiv.org/pdf/2303.03379v1 SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation Learning http://arxiv.org/pdf/2303.03340v1 Symbolic Synthesis of Neural Networks http://arxiv.org/pdf/2303.03192v1 Deep symbolic regression for physics guided by units constraints: toward the automated discovery of physical laws http://arxiv.org/pdf/2303.03227v1 Parallel Hybrid Networks: an interplay between quantum and classical neural networks http://arxiv.org/pdf/2303.03181v1 MetaPhysiCa: OOD Robustness in Physics-informed Machine Learning http://arxiv.org/pdf/2303.03157v1 Data-Driven Control with Inherent Lyapunov Stability http://arxiv.org/pdf/2303.03073v1 A neural network based model for multi-dimensional nonlinear Hawkes processes http://arxiv.org/pdf/2303.02890v1 An Analysis of Physics-Informed Neural Networks http://arxiv.org/pdf/2303.02844v1 Knowledge-embedded meta-learning model for lift coefficient prediction of airfoils http://arxiv.org/pdf/2303.02640v1 Swim: A General-Purpose, High-Performing, and Efficient Activation Function for Locomotion Control Tasks http://arxiv.org/pdf/2303.02535v1 Streaming Active Learning with Deep Neural Networks http://arxiv.org/pdf/2303.02505v1 Investigating Group Distributionally Robust Optimization for Deep Imbalanced Learning: A Case Study of Binary Tabular Data Classification http://arxiv.org/pdf/2303.02384v1 Hierarchical Training of Deep Neural Networks Using Early Exiting http://arxiv.org/pdf/2303.02338v1 Dynamic Deep Learning LES Closures: Online Optimization With Embedded DNS http://arxiv.org/pdf/2303.02310v1 IKD+: Reliable Low Complexity Deep Models For Retinopathy Classification http://arxiv.org/pdf/2303.02304v1 Coupled Multiwavelet Neural Operator Learning for Coupled Partial Differential Equations http://arxiv.org/pdf/2303.03386v1 Hierarchical Deep Learning Model for Degradation Prediction per Look-Ahead Scheduled Battery Usage Profile http://arxiv.org/pdf/2303.03247v1 Safety-Critical Control with Bounded Inputs via Reduced Order Models http://arxiv.org/pdf/2303.02456v1 Fixed-time Adaptive Neural Control for Physical Human-Robot Collaboration with Time-Varying Workspace Constraints http://arxiv.org/pdf/2303.02986v1 Non-intrusive data-driven reduced-order modeling for time-dependent parametrized problems http://arxiv.org/pdf/2303.02463v1 Efficient Quantum Algorithms for Nonlinear Stochastic Dynamical Systems http://arxiv.org/pdf/2303.02699v1 Expressiveness and Structure Preservation in Learning Port-Hamiltonian Systems http://arxiv.org/pdf/2303.03010v1 Tensor network reduced order models for wall-bounded flows http://arxiv.org/pdf/2303.02270v1 Simulating quantum error mitigation in fermionic encodings ./Link/2023-03-03 http://arxiv.org/pdf/2303.02063v1 Physics-Informed Deep Learning For Traffic State Estimation: A Survey and the Outlook http://arxiv.org/pdf/2303.01913v1 Bespoke: A Block-Level Neural Network Optimization Framework for Low-Cost Deployment http://arxiv.org/pdf/2303.01841v1 Anamnesic Neural Differential Equations with Orthogonal Polynomial Projections http://arxiv.org/pdf/2303.01826v1 TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents http://arxiv.org/pdf/2303.01801v1 Reservoir computing based on solitary-like waves dynamics of film flows: a proof of concept http://arxiv.org/pdf/2303.01767v1 Implicit Stochastic Gradient Descent for Training Physics-informed Neural Networks http://arxiv.org/pdf/2303.01693v2 Cross-domain Transfer Learning and State Inference for Soft Robots via a Semi-supervised Sequential Variational Bayes Framework http://arxiv.org/pdf/2303.01682v1 Neural-BO: A Black-box Optimization Algorithm using Deep Neural Networks http://arxiv.org/pdf/2303.01998v1 MLTEing Models: Negotiating, Evaluating, and Documenting Model and System Qualities http://arxiv.org/pdf/2303.01805v1 A variational quantum algorithm-based numerical method for solving potential and Stokes flows http://arxiv.org/pdf/2303.01820v1 Quantum-Error-Mitigation Circuit Groups for Noisy Quantum Metrology ./Link/2023-03-02 http://arxiv.org/pdf/2303.01488v1 Self-Improving Robots: End-to-End Autonomous Visuomotor Reinforcement Learning http://arxiv.org/pdf/2303.01483v1 Auxiliary Functions as Koopman Observables: Data-Driven Polynomial Optimization for Dynamical Systems http://arxiv.org/pdf/2303.01471v1 Quantum Hamiltonian Descent http://arxiv.org/pdf/2303.01462v1 Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization http://arxiv.org/pdf/2303.01193v1 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Masking to Deep Subband Filtering for Improved Dereverberation http://arxiv.org/pdf/2303.00466v1 ASP: Learn a Universal Neural Solver! http://arxiv.org/pdf/2303.00187v1 On the Integration of Physics-Based Machine Learning with Hierarchical Bayesian Modeling Techniques http://arxiv.org/pdf/2303.00170v1 Asymmetric Learning for Graph Neural Network based Link Prediction http://arxiv.org/pdf/2303.00633v1 An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization http://arxiv.org/pdf/2303.00192v1 Exploring Challenges and Opportunities to Support Designers in Learning to Co-create with AI-based Manufacturing Design Tools http://arxiv.org/pdf/2303.00474v1 Online Parameter Estimation using Physics-Informed Deep Learning for Vehicle Stability Algorithms http://arxiv.org/pdf/2303.00706v1 Predicting the wall-shear stress and wall pressure through convolutional neural networks ./Link/2023-02-28 http://arxiv.org/pdf/2302.14740v1 Fusion of ML with numerical simulation for optimized propeller design http://arxiv.org/pdf/2302.14376v1 GNOT: A General Neural Operator Transformer for Operator Learning http://arxiv.org/pdf/2302.14299v1 Gradient-Boosted Based Structured and Unstructured Learning http://arxiv.org/pdf/2302.14227v1 A unified scalable framework for causal sweeping strategies for Physics-Informed Neural Networks (PINNs) and their temporal decompositions http://arxiv.org/pdf/2302.14838v1 EvoPrompting: Language Models for Code-Level Neural Architecture Search http://arxiv.org/pdf/2302.14685v1 DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks http://arxiv.org/pdf/2302.14478v2 Scenarios and branch points to future machine intelligence http://arxiv.org/pdf/2302.14312v1 Auxiliary Task-based Deep Reinforcement Learning for Quantum Control http://arxiv.org/pdf/2302.14265v1 Neural Operators for Bypassing Gain and Control Computations in PDE Backstepping http://arxiv.org/pdf/2302.14526v1 Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling http://arxiv.org/pdf/2302.14512v1 A DuMux Framework for Data-Driven Multi-Scale Parametrizations http://arxiv.org/pdf/2302.14790v1 Sequential Quadratic Optimization for Stochastic Optimization with Deterministic Nonlinear Inequality and Equality Constraints http://arxiv.org/pdf/2302.14252v1 Compressed Decentralized Proximal Stochastic Gradient Method for Nonconvex Composite Problems with Heterogeneous Data http://arxiv.org/pdf/2302.14391v1 Log-law recovery through reinforcement-learning wall model for large-eddy simulation http://arxiv.org/pdf/2302.14592v1 Noise-assisted digital quantum simulation of open systems ./Link/2023-02-27 http://arxiv.org/pdf/2302.14040v1 Permutation Equivariant Neural Functionals http://arxiv.org/pdf/2302.14017v1 Full Stack Optimization of Transformer Inference: a Survey http://arxiv.org/pdf/2302.13995v1 Architecting Peer-to-Peer Serverless Distributed Machine Learning Training for Improved Fault Tolerance http://arxiv.org/pdf/2302.13693v1 Learning Topology-Specific Experts for Molecular Property Prediction http://arxiv.org/pdf/2302.13570v1 Physical Adversarial Attacks on Deep Neural Networks for Traffic Sign Recognition: A Feasibility Study http://arxiv.org/pdf/2302.13536v1 Natural Gradient Hybrid Variational Inference with Application to Deep Mixed Models http://arxiv.org/pdf/2302.13425v1 A Survey on Uncertainty Quantification Methods for Deep Neural Networks: An Uncertainty Source Perspective http://arxiv.org/pdf/2302.13408v1 Generative Models for 3D Point Clouds http://arxiv.org/pdf/2302.13397v1 Efficient physics-informed neural networks using hash encoding http://arxiv.org/pdf/2302.13271v1 Direct Estimation of Parameters in ODE Models Using WENDy: Weak-form Estimation of Nonlinear Dynamics http://arxiv.org/pdf/2302.13262v1 Invariant Neural Ordinary Differential Equations http://arxiv.org/pdf/2302.13163v1 Achieving High Accuracy with PINNs via Energy Natural Gradients http://arxiv.org/pdf/2302.13143v1 Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach http://arxiv.org/pdf/2302.13221v1 Data-Centric AI: Deep Generative Differentiable Feature Selection via Discrete Subsetting as Continuous Embedding Space Optimization http://arxiv.org/pdf/2302.13114v2 Sequential Query Encoding For Complex Query Answering on Knowledge Graphs http://arxiv.org/pdf/2302.13208v2 The wave operator representation of quantum and classical dynamics ./Link/2023-02-24 http://arxiv.org/pdf/2302.12705v1 Designing and simulating realistic spatial frequency domain imaging systems using open-source 3D rendering software http://arxiv.org/pdf/2302.12667v1 Deep active learning for nonlinear system identification http://arxiv.org/pdf/2302.12337v1 On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models http://arxiv.org/pdf/2302.07405v1 Unsupervised physics-informed neural network in reaction-diffusion biology models (Ulcerative colitis and Crohn's disease cases) A preliminary study http://arxiv.org/pdf/2302.07125v1 Stochastic Modified Flows, Mean-Field Limits and Dynamics of Stochastic Gradient Descent http://arxiv.org/pdf/2302.08798v1 On a probabilistic evolutionary approach to ocean modelling: From Lorenz-63 to idealized ocean models http://arxiv.org/pdf/2302.08796v2 Approaching epidemiological dynamics of COVID-19 with physics-informed neural networks http://arxiv.org/pdf/2302.08036v1 Detecting Stochastic Governing Laws with Observation on Stationary Distributions http://arxiv.org/pdf/2302.12682v1 A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling http://arxiv.org/pdf/2302.08309v1 The ADMM-PINNs Algorithmic Framework for Nonsmooth PDE-Constrained Optimization: A Deep Learning Approach http://arxiv.org/pdf/2302.07838v1 On diffeologies from infinite dimensional geometry to PDE constrained optimization http://arxiv.org/pdf/2302.05950v1 Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming http://arxiv.org/pdf/2302.04042v1 Data-driven control and transfer learning using neural canonical control structures* http://arxiv.org/pdf/2302.09741v1 Quantum computing of fluid dynamics using the hydrodynamic Schrödinger equation http://arxiv.org/pdf/2302.09559v1 Physics-guided deep reinforcement learning for flow field denoising http://arxiv.org/pdf/2302.08780v1 SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets http://arxiv.org/pdf/2302.08199v1 Deep learning based surrogate modeling for thermal plume prediction of groundwater heat pumps http://arxiv.org/pdf/2302.07334v1 Bayesian uncertainty quantification framework for wake model calibration and validation with historical wind farm power data http://arxiv.org/pdf/2302.06186v3 Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine 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networks with multi-compartmental neurons and non-Hebbian plasticity http://arxiv.org/pdf/2302.08981v1 Black-Box Batch Active Learning for Regression http://arxiv.org/pdf/2302.08893v1 A survey on online active learning http://arxiv.org/pdf/2302.08606v1 Intrinsic and extrinsic deep learning on manifolds http://arxiv.org/pdf/2302.08415v1 Temporal Graph Neural Networks for Irregular Data http://arxiv.org/pdf/2302.08406v1 Entity Aware Modelling: A Survey http://arxiv.org/pdf/2302.08347v1 The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems http://arxiv.org/pdf/2302.07503v1 Excess risk bound for deep learning under weak dependence http://arxiv.org/pdf/2302.07426v1 Efficiently Learning Neural Networks: What Assumptions May Suffice? http://arxiv.org/pdf/2302.07071v1 Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics http://arxiv.org/pdf/2302.05925v1 Physics informed WNO http://arxiv.org/pdf/2302.05882v1 From high-dimensional & mean-field dynamics to dimensionless ODEs: A unifying approach to SGD in two-layers networks http://arxiv.org/pdf/2302.05793v1 Distributional GFlowNets with Quantile Flows http://arxiv.org/pdf/2302.06391v1 Incorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework http://arxiv.org/pdf/2302.04730v1 A Benchmark on Uncertainty Quantification for Deep Learning Prognostics http://arxiv.org/pdf/2302.04400v1 Discovering interpretable Lagrangian of dynamical systems from data http://arxiv.org/pdf/2302.04019v1 Fortuna: A Library for Uncertainty Quantification in Deep Learning http://arxiv.org/pdf/2302.12682v1 A DeepONet Multi-Fidelity Approach for Residual Learning in Reduced Order Modeling http://arxiv.org/pdf/2302.12617v1 Leveraging Jumpy Models for Planning and Fast Learning in Robotic Domains http://arxiv.org/pdf/2302.12563v1 Retrieved Sequence Augmentation for Protein Representation Learning http://arxiv.org/pdf/2302.12545v1 Hybrid machine-learned homogenization: Bayesian data mining and convolutional neural networks http://arxiv.org/pdf/2302.12431v1 Flexible Phase Dynamics for Bio-Plausible Contrastive Learning http://arxiv.org/pdf/2302.12667v1 Deep active learning for nonlinear system identification http://arxiv.org/pdf/2302.12477v1 Frequency and Scale Perspectives of Feature Extraction ./Link/2023-02-23 http://arxiv.org/pdf/2302.12235v1 Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows http://arxiv.org/pdf/2302.12168v1 A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers http://arxiv.org/pdf/2302.11978v1 Does Deep Learning Learn to Abstract? 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http://arxiv.org/pdf/2302.11354v1 Learning Dynamic Graph Embeddings with Neural Controlled Differential Equations http://arxiv.org/pdf/2302.11101v1 Learning from Predictions: Fusing Training and Autoregressive Inference for Long-Term Spatiotemporal Forecasts http://arxiv.org/pdf/2302.11085v1 Learning to Generalize Provably in Learning to Optimize http://arxiv.org/pdf/2302.11049v1 Framework for Certification of AI-Based Systems http://arxiv.org/pdf/2302.11012v1 Posterior Annealing: Fast Calibrated Uncertainty for Regression http://arxiv.org/pdf/2302.11007v1 Unification of popular artificial neural network activation functions http://arxiv.org/pdf/2302.11006v1 Data-driven reduced-order modelling for blood flow simulations with geometry-informed snapshots http://arxiv.org/pdf/2302.11000v1 CHA2: CHemistry Aware Convex Hull Autoencoder Towards Inverse Molecular Design http://arxiv.org/pdf/2302.10975v1 Improved uncertainty quantification for neural networks with Bayesian last layer http://arxiv.org/pdf/2302.10952v1 Machine learning for the prediction of safe and biologically active organophosphorus molecules http://arxiv.org/pdf/2302.10835v1 A General-Purpose Transferable Predictor for Neural Architecture Search http://arxiv.org/pdf/2302.10692v1 On Inductive Biases for Machine Learning in Data Constrained Settings http://arxiv.org/pdf/2302.10433v1 On discrete symmetries of robotics systems: A group-theoretic and data-driven analysis http://arxiv.org/pdf/2302.10406v1 Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines http://arxiv.org/pdf/2302.10351v1 Variational Autoencoding Neural Operators http://arxiv.org/pdf/2302.10347v1 Online Evolutionary Neural Architecture Search for Multivariate Non-Stationary Time Series Forecasting http://arxiv.org/pdf/2302.10322v1 Deep Transformers without Shortcuts: Modifying Self-attention for Faithful Signal Propagation http://arxiv.org/pdf/2302.10255v1 NeuralStagger: accelerating physics-constrained neural PDE solver with spatial-temporal decomposition http://arxiv.org/pdf/2302.10175v1 Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies http://arxiv.org/pdf/2302.10062v1 An evaluation of deep learning models for predicting water depth evolution in urban floods http://arxiv.org/pdf/2302.09748v1 Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles http://arxiv.org/pdf/2302.09668v1 Physics-aware deep learning framework for linear elasticity http://arxiv.org/pdf/2302.09574v1 Guided Deep Kernel Learning http://arxiv.org/pdf/2302.09566v1 Optimization Methods in Deep Learning: A Comprehensive Overview http://arxiv.org/pdf/2302.09526v1 Mixed Semi-Supervised Generalized-Linear-Regression with applications to Deep learning http://arxiv.org/pdf/2302.09465v1 Stochastic Generative Flow Networks 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http://arxiv.org/pdf/2302.08560v1 Imitation from Arbitrary Experience: A Dual Unification of Reinforcement and Imitation Learning Methods http://arxiv.org/pdf/2302.09071v1 Rejecting Cognitivism: Computational Phenomenology for Deep Learning http://arxiv.org/pdf/2302.10803v1 Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers http://arxiv.org/pdf/2302.07350v1 Graph schemas as abstractions for transfer learning, inference, and planning http://arxiv.org/pdf/2302.08329v1 Conditional deep generative models as surrogates for spatial field solution reconstruction with quantified uncertainty in Structural Health Monitoring applications http://arxiv.org/pdf/2302.07260v1 Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks http://arxiv.org/pdf/2302.11405v1 ML-driven Hardware Cost Model for MLIR http://arxiv.org/pdf/2302.06852v1 Using Artificial Intelligence to aid Scientific Discovery of Climate Tipping Points http://arxiv.org/pdf/2302.10909v1 Multi-modal Machine Learning in Engineering Design: A Review and Future Directions http://arxiv.org/pdf/2302.11327v2 A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks http://arxiv.org/pdf/2302.07608v1 Uncertainty-Estimation with Normalized Logits for Out-of-Distribution Detection http://arxiv.org/pdf/2302.10864v1 Reinforcement Learning-based Control of Nonlinear Systems using Carleman Approximation: Structured and Unstructured Designs http://arxiv.org/pdf/2302.10104v1 Comprehensive Framework for Controlling Nonlinear Multi-Species Water Quality Dynamics http://arxiv.org/pdf/2302.09521v1 Rank-Minimizing and Structured Model Inference http://arxiv.org/pdf/2302.05630v1 CILP: Co-simulation based Imitation Learner for Dynamic Resource Provisioning in Cloud Computing Environments http://arxiv.org/pdf/2302.05498v1 Data-Driven Inverse Optimization for Offer Price Recovery http://arxiv.org/pdf/2302.04344v1 Learning Dynamical Systems by Leveraging Data from Similar Systems http://arxiv.org/pdf/2302.12336v1 Physics Informed Deep Learning: Applications in Transportation http://arxiv.org/pdf/2302.11024v1 Gradient Flows for Sampling: Mean-Field Models, Gaussian Approximations and Affine Invariance http://arxiv.org/pdf/2302.10448v1 Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems http://arxiv.org/pdf/2302.10424v1 Deep Learning via Neural Energy Descent http://arxiv.org/pdf/2302.09233v1 Solving Boltzmann equation with neural sparse representation http://arxiv.org/pdf/2302.08263v1 Meta-Auto-Decoder: A Meta-Learning Based Reduced Order Model for Solving Parametric Partial Differential Equations http://arxiv.org/pdf/2302.08232v1 Learning discrete Lagrangians for variationalPDEs from data and detection of travelling waves http://arxiv.org/pdf/2302.08216v1 Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression http://arxiv.org/pdf/2302.08166v1 Laplace neural operator for complex geometries http://arxiv.org/pdf/2302.08035v1 Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions http://arxiv.org/pdf/2302.08022v1 Kernel-free boundary integral method for two-phase Stokes equations with discontinuous viscosity on staggered grids http://arxiv.org/pdf/2302.06479v1 Structure-Preserving Model Reduction for Port-Hamiltonian Systems Based on a Special Class of Nonlinear Approximation Ansatzes http://arxiv.org/pdf/2302.05594v1 An Efficient Spectral Trust-Region Deflation Method for Multiple Solutions http://arxiv.org/pdf/2302.05419v1 Gauge-equivariant neural networks as preconditioners in lattice QCD http://arxiv.org/pdf/2302.05356v1 Approximation and Structured Prediction with Sparse Wasserstein Barycenters http://arxiv.org/pdf/2302.05322v1 Numerical Methods For PDEs Over Manifolds Using Spectral Physics Informed Neural Networks http://arxiv.org/pdf/2302.05104v1 Monte Carlo Neural Operator for Learning PDEs via Probabilistic Representation http://arxiv.org/pdf/2302.10899v1 Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data http://arxiv.org/pdf/2302.04518v1 Introduction To Gaussian Process Regression In Bayesian Inverse Problems, With New ResultsOn Experimental Design For Weighted Error Measures http://arxiv.org/pdf/2302.04107v1 Can Physics-Informed Neural Networks beat the Finite Element Method? ./Link/2023-02-09 ./Link/2023-02-08 ./Link/2023-02-07 http://arxiv.org/pdf/2302.03663v1 SDYN-GANs: Adversarial Learning Methods for Multistep Generative Models for General Order Stochastic Dynamics http://arxiv.org/pdf/2302.03580v1 Multi-Scale Message Passing Neural PDE Solvers http://arxiv.org/pdf/2302.03379v1 Data augmentation for machine learning of chemical process flowsheets http://arxiv.org/pdf/2302.03375v1 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http://arxiv.org/pdf/2302.02522v1 Prior Density Learning in Variational Bayesian Phylogenetic Parameters Inference http://arxiv.org/pdf/2302.02420v1 Direct Uncertainty Quantification http://arxiv.org/pdf/2302.02406v1 Pre-screening breast cancer with machine learning and deep learning http://arxiv.org/pdf/2302.02333v1 Learning in quantum games http://arxiv.org/pdf/2302.02140v1 Dynamical Equations With Bottom-up Self-Organizing Properties Learn Accurate Dynamical Hierarchies Without Any Loss Function http://arxiv.org/pdf/2302.01934v1 A neural operator-based surrogate solver for free-form electromagnetic inverse design http://arxiv.org/pdf/2302.02101v1 GRANDE: a neural model over directed multigraphs with application to anti-money laundering http://arxiv.org/pdf/2302.02055v1 Harnessing Simulation for Molecular Embeddings http://arxiv.org/pdf/2302.02004v1 Koopman Operator Learning: Sharp Spectral Rates and Spurious Eigenvalues http://arxiv.org/pdf/2302.01961v1 Asymmetric Certified Robustness via Feature-Convex Neural Networks http://arxiv.org/pdf/2302.01952v1 On a continuous time model of gradient descent dynamics and instability in deep learning http://arxiv.org/pdf/2302.01810v1 PINN Training using Biobjective Optimization: The Trade-off between Data Loss and Residual Loss http://arxiv.org/pdf/2302.01687v1 Better Training of GFlowNets with Local Credit and Incomplete Trajectories http://arxiv.org/pdf/2302.01538v2 DCM: Deep energy method based on the principle of minimum complementary energy http://arxiv.org/pdf/2302.01518v1 LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry http://arxiv.org/pdf/2302.01497v1 Gradient Estimation for Unseen Domain Risk Minimization with Pre-Trained Models http://arxiv.org/pdf/2302.01440v1 Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications http://arxiv.org/pdf/2302.01259v1 Geometric Deep Learning for Autonomous Driving: Unlocking the Power of Graph Neural Networks With CommonRoad-Geometric http://arxiv.org/pdf/2302.01746v1 Machine Learning Extreme Acoustic Non-reciprocity in a Linear Waveguide with Multiple Nonlinear Asymmetric Gates http://arxiv.org/pdf/2302.00753v1 High-precision regressors for particle physics http://arxiv.org/pdf/2302.01178v1 Convolutional Neural Operators http://arxiv.org/pdf/2302.01060v1 Physics Constrained Motion Prediction with Uncertainty Quantification http://arxiv.org/pdf/2302.01052v1 Site-specific Deep Learning Path Loss Models based on the Method of Moments http://arxiv.org/pdf/2302.01051v1 Randomized prior wavelet neural operator for uncertainty quantification http://arxiv.org/pdf/2302.01020v1 Meta Learning in Decentralized Neural Networks: Towards More General AI http://arxiv.org/pdf/2302.01002v1 Over-parameterised Shallow Neural Networks with Asymmetrical Node Scaling: Global Convergence Guarantees and Feature Learning http://arxiv.org/pdf/2302.00938v1 An Enhanced V-cycle MgNet Model for Operator Learning in Numerical Partial Differential Equations http://arxiv.org/pdf/2302.00878v1 The Contextual Lasso: Sparse Linear Models via Deep Neural Networks http://arxiv.org/pdf/2302.00860v1 Interventional and Counterfactual Inference with Diffusion Models http://arxiv.org/pdf/2302.00855v1 Molecular Geometry-aware Transformer for accurate 3D Atomic System modeling http://arxiv.org/pdf/2302.00854v1 Learning PDE Solution Operator for Continuous Modeling of Time-Series http://arxiv.org/pdf/2302.00773v2 Neural Networks for Symbolic Regression http://arxiv.org/pdf/2302.00755v1 Hierarchical shrinkage Gaussian processes: applications to computer code emulation and dynamical system recovery http://arxiv.org/pdf/2302.00658v1 Graph Neural Operators for Classification of Spatial Transcriptomics Data http://arxiv.org/pdf/2302.00633v2 Deep Dependency Networks for Multi-Label Classification http://arxiv.org/pdf/2302.00615v1 GFlowNets for AI-Driven Scientific Discovery http://arxiv.org/pdf/2302.00600v1 Two for One: Diffusion Models and Force Fields for Coarse-Grained Molecular Dynamics http://arxiv.org/pdf/2302.00557v1 Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design http://arxiv.org/pdf/2302.00514v1 Towards Implementing Energy-aware Data-driven Intelligence for Smart Health Applications on Mobile Platforms http://arxiv.org/pdf/2302.00457v1 Simplicity Bias in 1-Hidden Layer Neural Networks http://arxiv.org/pdf/2302.00374v1 HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks http://arxiv.org/pdf/2302.02302v1 Achieving Robust Generalization for Wireless Channel Estimation Neural Networks by Designed Training Data http://arxiv.org/pdf/2302.01806v1 Mitigating Data Scarcity for Large Language Models http://arxiv.org/pdf/2302.01802v1 FR3D: Three-dimensional Flow Reconstruction and Force Estimation for Unsteady Flows Around Arbitrary Bluff Bodies via Conformal Mapping Aided Convolutional Autoencoders http://arxiv.org/pdf/2302.00990v1 Physics 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shallow water moment equations: A study using POD-Galerkin and dynamical low rank approximation http://arxiv.org/pdf/2302.00752v1 Sparse Spectral Methods for Solving High-Dimensional and Multiscale Elliptic PDEs http://arxiv.org/pdf/2301.13547v1 Machine learning of evolving physics-based material models for multiscale solid mechanics http://arxiv.org/pdf/2302.00045v1 Neural Control of Parametric Solutions for High-dimensional Evolution PDEs http://arxiv.org/pdf/2302.02076v1 AONN: An adjoint-oriented neural network method for all-at-once solutions of parametric optimal control problems http://arxiv.org/pdf/2302.02980v1 Hybrid Genetic Optimisation for Quantum Feature Map Design http://arxiv.org/pdf/2302.02278v1 Optimization Applications as Quantum Performance Benchmarks http://arxiv.org/pdf/2302.01962v1 Correspondence between open bosonic systems and stochastic differential equations http://arxiv.org/pdf/2302.01413v1 Solving two-dimensional quantum eigenvalue problems using 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predicting aortic hemodynamics obtained by 4D flow MRI http://arxiv.org/pdf/2301.02120v1 Reprogramming Pretrained Language Models for Protein Sequence Representation Learning http://arxiv.org/pdf/2301.01947v1 StitchNet: Composing Neural Networks from Pre-Trained Fragments http://arxiv.org/pdf/2301.02024v1 Port-Hamiltonian Systems Modelling in Electrical Engineering ./Link/2023-01-04 http://arxiv.org/pdf/2301.01720v1 Augmenting data-driven models for energy systems through feature engineering: A Python framework for feature engineering http://arxiv.org/pdf/2301.01718v1 An adaptive, training-free reduced-order model for convection-dominated problems based on hybrid snapshots http://arxiv.org/pdf/2301.01699v1 Data-driven modelling of turbine wake interactions and flow resistance in large wind farms ./Link/2023-01-03 http://arxiv.org/pdf/2301.01104v1 KoopmanLab: A PyTorch module of Koopman neural operator family for solving partial differential equations http://arxiv.org/pdf/2301.01047v1 A Theory of Human-Like Few-Shot Learning http://arxiv.org/pdf/2301.01038v1 Heterogeneous Domain Adaptation and Equipment Matching: DANN-based Alignment with Cyclic Supervision (DBACS) http://arxiv.org/pdf/2301.00957v1 Meta-learning generalizable dynamics from trajectories http://arxiv.org/pdf/2301.00942v1 Deep Learning and Computational Physics (Lecture Notes) http://arxiv.org/pdf/2301.00951v1 Digital Engineering Transformation with Trustworthy AI towards Industry 4.0: Emerging Paradigm Shifts http://arxiv.org/pdf/2301.01102v1 Fourier series (based) multiscale method for computational analysis in science and engineering: VII. 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http://arxiv.org/pdf/2212.02336v2 Preparing Quantum States by Measurement-feedback Control with Bayesian Optimization http://arxiv.org/pdf/2212.02204v1 Can neural quantum states learn volume-law ground states? ./Link/2022-12-02 http://arxiv.org/pdf/2212.01371v1 Adaptive Robust Model Predictive Control via Uncertainty Cancellation http://arxiv.org/pdf/2212.01346v1 Guaranteed Conformance of Neurosymbolic Models to Natural Constraints http://arxiv.org/pdf/2212.01232v1 Loss shaping enhances exact gradient learning with EventProp in Spiking Neural Networks http://arxiv.org/pdf/2212.01168v1 Identifying Hamiltonian manifold in neural networks http://arxiv.org/pdf/2212.01046v1 Improved Representation Learning Through Tensorized Autoencoders http://arxiv.org/pdf/2212.01016v1 Accelerating Inverse Learning via Intelligent Localization with Exploratory Sampling http://arxiv.org/pdf/2212.00998v1 Credit Assignment for Trained Neural Networks Based on Koopman Operator Theory http://arxiv.org/pdf/2212.00970v1 Bayesian Physics Informed Neural Networks for Data Assimilation and Spatio-Temporal Modelling of Wildfires http://arxiv.org/pdf/2212.01303v1 Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning http://arxiv.org/pdf/2212.01054v1 Model and Data Agreement for Learning with Noisy Labels http://arxiv.org/pdf/2212.00961v1 Modeling and Optimization of Steady Flow of Natural Gas and Hydrogen Mixtures in Pipeline Networks http://arxiv.org/pdf/2212.01014v1 Reinforcement-learning-based control of convectively-unstable flows ./Link/2022-12-01 http://arxiv.org/pdf/2212.00772v1 Uniform versus uncertainty sampling: When being active is less efficient than staying passive http://arxiv.org/pdf/2212.00554v1 Early prediction of the risk of ICU mortality with Deep Federated Learning http://arxiv.org/pdf/2212.00270v1 On the Compatibility between a Neural Network and a Partial Differential Equation for Physics-informed Learning http://arxiv.org/pdf/2212.00253v1 Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox http://arxiv.org/pdf/2212.00222v1 Experimental Observations of the Topology of Convolutional Neural Network Activations http://arxiv.org/pdf/2212.00217v1 Physics-Constrained Generative Adversarial Networks for 3D Turbulence http://arxiv.org/pdf/2212.00186v1 Multi-Task Imitation Learning for Linear Dynamical Systems http://arxiv.org/pdf/2212.00624v1 Safe Control Design for Unknown Nonlinear Systems with Koopman-based Fixed-Time Identification http://arxiv.org/pdf/2212.00491v1 Gradient and Channel Aware Dynamic Scheduling for Over-the-Air Computation in Federated Edge Learning Systems http://arxiv.org/pdf/2212.00268v1 Gaussian Process Barrier States for Safe Trajectory Optimization and Control http://arxiv.org/pdf/2212.00332v1 Invariant Data-driven Subgrid Stress Modeling on Anisotropic Grids for Large Eddy Simulation http://arxiv.org/pdf/2212.00205v1 Taylor-Couette flow of hard-sphere suspensions: Overview of current understanding http://arxiv.org/pdf/2212.00782v1 Variational Neural-Network Ansatz for Continuum Quantum Field Theory ./Link/2022-11-30 http://arxiv.org/pdf/2211.17228v1 AIO-P: Expanding Neural Performance Predictors Beyond Image Classification http://arxiv.org/pdf/2211.17226v1 GENNAPE: Towards Generalized Neural Architecture Performance Estimators http://arxiv.org/pdf/2211.17158v1 Proximal Residual Flows for Bayesian Inverse Problems http://arxiv.org/pdf/2211.16943v1 Predicting Properties of Quantum Systems with Conditional Generative Models http://arxiv.org/pdf/2211.16838v1 Towards Improving Exploration in Self-Imitation Learning using Intrinsic Motivation http://arxiv.org/pdf/2211.16808v1 Efficient Adversarial Input Generation via Neural Net Patching http://arxiv.org/pdf/2211.16753v1 VI-PINNs: Variance-involved Physics-informed Neural Networks for Fast and Accurate Prediction of Partial Differential Equations http://arxiv.org/pdf/2211.16731v2 Towards Training GNNs using Explanation Directed Message Passing http://arxiv.org/pdf/2211.16684v1 Capturing long-range interaction with reciprocal space neural network http://arxiv.org/pdf/2211.16667v1 Dynamic Sparse Training via Balancing the Exploration-Exploitation Trade-off http://arxiv.org/pdf/2211.16653v1 CRU: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data http://arxiv.org/pdf/2211.16869v1 NeAF: Learning Neural Angle Fields for Point Normal Estimation http://arxiv.org/pdf/2211.17191v1 Direct data-driven LPV control of nonlinear systems: An experimental result http://arxiv.org/pdf/2211.16676v1 Robust Learning of Nonlinear Dynamical Systems with Safety and Stability Properties ./Link/2022-11-29 http://arxiv.org/pdf/2211.16495v1 Graph Neural Networks: A Powerful and Versatile Tool for Advancing Design, Reliability, and Security of ICs http://arxiv.org/pdf/2211.16277v1 Differentiable User Models 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strategies using deep learning and tree search http://arxiv.org/pdf/2211.15044v1 Machine Learning Accelerated PDE Backstepping Observers http://arxiv.org/pdf/2211.14946v1 Self-Destructing Models: Increasing the Costs of Harmful Dual Uses in Foundation Models http://arxiv.org/pdf/2211.14819v1 Deep Active Learning for Computer Vision: Past and Future http://arxiv.org/pdf/2211.14680v1 A Physics-informed Diffusion Model for High-fidelity Flow Field Reconstruction http://arxiv.org/pdf/2211.14605v1 Looking at the posterior: on the origin of uncertainty in neural-network classification http://arxiv.org/pdf/2211.14503v1 Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth http://arxiv.org/pdf/2211.14493v1 Multi-fidelity Gaussian Process for Biomanufacturing Process Modeling with Small Data http://arxiv.org/pdf/2211.14492v1 Enhancing Constraint Programming via Supervised Learning for Job Shop Scheduling http://arxiv.org/pdf/2211.15197v1 Metric Learning as a Service with Covariance Embedding http://arxiv.org/pdf/2211.15104v1 Approximate Predictive Control Barrier Functions using Neural Networks: A Computationally Cheap and Permissive Safety Filter http://arxiv.org/pdf/2211.14455v1 Information Geometry of Dynamics on Graphs and Hypergraphs http://arxiv.org/pdf/2211.15269v1 Machine-learning-assisted construction of appropriate rotating frame http://arxiv.org/pdf/2211.15209v1 Deep learning optimal quantum annealing schedules for random Ising models ./Link/2022-11-25 http://arxiv.org/pdf/2211.14302v1 Neural DAEs: Constrained neural networks http://arxiv.org/pdf/2211.14296v1 A System for Morphology-Task Generalization via Unified Representation and Behavior Distillation http://arxiv.org/pdf/2211.13979v1 BatmanNet: Bi-branch Masked Graph Transformer Autoencoder for Molecular Representation http://arxiv.org/pdf/2211.13853v1 Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry 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Approximately Orthogonal Training Framework in Deep Neural Networks http://arxiv.org/pdf/2211.14249v1 Neural Poisson: Indicator Functions for Neural Fields http://arxiv.org/pdf/2211.14020v1 SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow http://arxiv.org/pdf/2211.13942v1 Affine Transformation Edited and Refined Deep Neural Network for Quantitative Susceptibility Mapping http://arxiv.org/pdf/2211.13507v1 Identifiability of nonlinear ODE Models with Time-Varying Parameters: the General Analytical Solution and Applications in Viral Dynamics http://arxiv.org/pdf/2211.14119v1 A reduced-order model for dynamic simulation of district heating networks http://arxiv.org/pdf/2211.13944v1 DMIS: Dynamic Mesh-based Importance Sampling for Training Physics-Informed Neural Networks http://arxiv.org/pdf/2211.13420v1 Projection pursuit adaptation on polynomial chaos expansions http://arxiv.org/pdf/2211.13418v1 AI-augmented stabilized finite element method 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Koopman Network http://arxiv.org/pdf/2211.09392v1 Data Dimension Reduction makes ML Algorithms efficient http://arxiv.org/pdf/2211.09380v1 Multilayer Perceptron-based Surrogate Models for Finite Element Analysis http://arxiv.org/pdf/2211.09557v1 Deep Learning for Optimal Volt/VAR Control using Distributed Energy Resources ./Link/2022-11-16 http://arxiv.org/pdf/2211.08939v1 Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology http://arxiv.org/pdf/2211.08900v1 Convergence analysis of unsupervised Legendre-Galerkin neural networks for linear second-order elliptic PDEs http://arxiv.org/pdf/2211.08856v1 Challenges in creative generative models for music: a divergence maximization perspective http://arxiv.org/pdf/2211.08854v1 Graph Filters for Signal Processing and Machine Learning on Graphs http://arxiv.org/pdf/2211.08771v1 Symmetries in the dynamics of wide two-layer neural networks http://arxiv.org/pdf/2211.08760v1 SVD-PINNs: 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http://arxiv.org/pdf/2211.09679v1 Dimensional homogeneity constrained gene expression programming for discovering governing equations from noisy and scarce data ./Link/2022-11-15 http://arxiv.org/pdf/2211.08243v1 Neural Bayesian Network Understudy http://arxiv.org/pdf/2211.08179v1 Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions http://arxiv.org/pdf/2211.08081v1 Autonomous Golf Putting with Data-Driven and Physics-Based Methods http://arxiv.org/pdf/2211.08064v1 Physics-Informed Machine Learning: A Survey on Problems, Methods and Applications http://arxiv.org/pdf/2211.07931v1 Personalized Federated Learning with Multi-branch Architecture http://arxiv.org/pdf/2211.07909v1 Selective Memory Recursive Least Squares: Uniformly Allocated Approximation Capabilities of RBF Neural Networks in Real-Time Learning http://arxiv.org/pdf/2211.07885v1 Using Human Perception to Regularize Transfer Learning 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http://arxiv.org/pdf/2211.06603v1 On the High Symmetry of Neural Network Functions http://arxiv.org/pdf/2211.06566v1 Innovative Drug-like Molecule Generation from Flow-based Generative Model http://arxiv.org/pdf/2211.06553v1 Lifelong and Continual Learning Dialogue Systems http://arxiv.org/pdf/2211.06524v1 Quantum Split Neural Network Learning using Cross-Channel Pooling http://arxiv.org/pdf/2211.06701v1 Structure-Preserving 3D Garment Modeling with Neural Sewing Machines http://arxiv.org/pdf/2211.07112v1 Koopman Bilinearization of Nonlinear Control Systems http://arxiv.org/pdf/2211.07011v1 High Order Schemes for Gradient Flow with Respect to a Metric http://arxiv.org/pdf/2211.07209v1 Learning Neural Optimal Interpolation Models and Solvers http://arxiv.org/pdf/2211.07408v1 Controlling Quantum Chaos: Optimal Coherent Targeting http://arxiv.org/pdf/2211.07629v1 Assessing requirements to scale to practical quantum advantage http://arxiv.org/pdf/2211.06907v1 Calculation of molecular 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Detecting Human-understandable Concepts http://arxiv.org/pdf/2211.05777v1 Hybrid quantum neural network for drug response prediction http://arxiv.org/pdf/2211.05723v1 A Python library for nonlinear system identification using Multi-Gene Genetic Programming algorithm http://arxiv.org/pdf/2211.05639v1 Gaussian inference for data-driven state-feedback design of nonlinear systems http://arxiv.org/pdf/2211.05560v1 Finite basis physics-informed neural networks as a Schwarz domain decomposition method http://arxiv.org/pdf/2211.05326v1 A PIE Representation of Coupled Linear ODE-PDE Systems with Constant Delay and Stability Analysis using LPIs http://arxiv.org/pdf/2211.05365v1 Constructing Dynamical Systems to Model Higher Order Ising Spin Interactions and their Application in Solving Combinatorial Optimization Problems http://arxiv.org/pdf/2211.05370v1 Data-driven Topology Optimization (DDTO) for Three-dimensional Continuum Structures http://arxiv.org/pdf/2211.05262v1 Stabilizing Machine 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Graph Neural Network http://arxiv.org/pdf/2211.03481v1 Predictive Coding beyond Gaussian Distributions http://arxiv.org/pdf/2211.03374v1 Deep Causal Learning: Representation, Discovery and Inference http://arxiv.org/pdf/2211.03329v1 Implicit Graphon Neural Representation http://arxiv.org/pdf/2211.03241v1 Neural PDE Solvers for Irregular Domains http://arxiv.org/pdf/2211.03198v1 Gauge Equivariant Neural Networks for 2+1D U(1) Gauge Theory Simulations in Hamiltonian Formulation http://arxiv.org/pdf/2211.03169v1 Learning Riemannian Stable Dynamical Systems via Diffeomorphisms http://arxiv.org/pdf/2211.03033v1 Efficient Traffic State Forecasting using Spatio-Temporal Network Dependencies: A Sparse Graph Neural Network Approach http://arxiv.org/pdf/2211.03022v1 Physics Informed Machine Learning for Chemistry Tabulation http://arxiv.org/pdf/2211.02941v1 Small Language Models for Tabular Data http://arxiv.org/pdf/2211.02830v1 Discovering ordinary differential equations that govern time-series 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http://arxiv.org/pdf/2210.10443v1 Deep neural network expressivity for optimal stopping problems http://arxiv.org/pdf/2210.10360v1 Adaptive Neural Network Ensemble Using Frequency Distribution http://arxiv.org/pdf/2210.10292v1 Comparing Spectroscopy Measurements in the Prediction of in Vitro Dissolution Profile using Artificial Neural Networks http://arxiv.org/pdf/2210.10311v1 Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning ./Link/2022-10-18 http://arxiv.org/pdf/2210.09343v1 Data-Driven Observability Decomposition with Koopman Operators for Optimization of Output Functions of Nonlinear Systems http://arxiv.org/pdf/2210.09546v1 An Improved Structured Mesh Generation Method Based on Physics-informed Neural Networks http://arxiv.org/pdf/2210.09424v1 State estimation in minimal turbulent channel flow: A comparative study of 4DVar and PINN http://arxiv.org/pdf/2210.09007v1 A non-Hermitian Ground State Searching Algorithm Enhanced by 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http://arxiv.org/pdf/2210.09082v1 A Solver-Free Framework for Scalable Learning in Neural ILP Architectures http://arxiv.org/pdf/2210.09081v1 Asymptotic-Preserving Neural Networks for hyperbolic systems with diffusive scaling http://arxiv.org/pdf/2210.08942v1 Meta-Learning via Classifier(-free) Guidance http://arxiv.org/pdf/2210.08877v1 Data-Driven Short-Term Daily Operational Sea Ice Regional Forecasting http://arxiv.org/pdf/2210.08566v1 Theory for Equivariant Quantum Neural Networks http://arxiv.org/pdf/2210.08424v1 A cusp-capturing PINN for elliptic interface problems http://arxiv.org/pdf/2210.08367v1 Active Learning with Neural Networks: Insights from Nonparametric Statistics http://arxiv.org/pdf/2210.08343v1 Modular machine learning-based elastoplasticity: generalization in the context of limited data http://arxiv.org/pdf/2210.08342v1 Well-definedness of Physical Law Learning: The Uniqueness Problem http://arxiv.org/pdf/2210.08243v1 Substructure-Atom Cross Attention for Molecular Representation Learning http://arxiv.org/pdf/2210.08186v1 Machine Learning Approach for Predicting Students Academic Performance and Study Strategies based on their Motivation http://arxiv.org/pdf/2210.08608v1 Posterior Regularized Bayesian Neural Network Incorporating Soft and Hard Knowledge Constraints http://arxiv.org/pdf/2210.08826v1 Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels http://arxiv.org/pdf/2210.08780v1 Sample-efficient Model Predictive Control Design of Soft Robotics by Bayesian Optimization http://arxiv.org/pdf/2210.09044v1 Hyper-differential sensitivity analysis with respect to model discrepancy: Calibration and optimal solution updating http://arxiv.org/pdf/2210.09037v1 Hyper-differential sensitivity analysis with respect to model discrepancy: mathematics and computation ./Link/2022-10-14 http://arxiv.org/pdf/2210.07932v1 Neural Routing in Meta Learning http://arxiv.org/pdf/2210.07880v1 Tunable Complexity Benchmarks for Evaluating Physics-Informed Neural Networks on Coupled Ordinary Differential Equations http://arxiv.org/pdf/2210.07439v1 Risk-Awareness in Learning Neural Controllers for Temporal Logic Objectives http://arxiv.org/pdf/2210.07762v2 Controllable Style Transfer via Test-time Training of Implicit Neural Representation http://arxiv.org/pdf/2210.07582v1 Deep PatchMatch MVS with Learned Patch Coplanarity, Geometric Consistency and Adaptive Pixel Sampling http://arxiv.org/pdf/2210.07480v1 Real-time computational powered landing guidance using convex optimization and neural networks http://arxiv.org/pdf/2210.07900v1 A descent algorithm for the optimal control of ReLU neural network informed PDEs based on approximate directional derivatives ./Link/2022-10-13 http://arxiv.org/pdf/2210.07237v1 Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations http://arxiv.org/pdf/2210.07182v1 PDEBENCH: An Extensive Benchmark for Scientific Machine Learning http://arxiv.org/pdf/2210.07024v1 Self-explaining deep models with logic rule reasoning http://arxiv.org/pdf/2210.06888v1 AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks through Accuracy Gradient http://arxiv.org/pdf/2210.06876v1 Learning Physical Dynamics with Subequivariant Graph Neural Networks http://arxiv.org/pdf/2210.06662v1 Action Matching: A Variational Method for Learning Stochastic Dynamics from Samples http://arxiv.org/pdf/2210.06806v1 OOOE: Only-One-Object-Exists Assumption to Find Very Small Objects in Chest Radiographs http://arxiv.org/pdf/2210.07098v1 Meta-learning Based Short-Term Passenger Flow Prediction for Newly-Operated Urban Rail Transit Stations http://arxiv.org/pdf/2210.07082v1 Implicit Bias in Leaky ReLU Networks Trained on High-Dimensional Data ./Link/2022-10-12 http://arxiv.org/pdf/2210.06464v2 Predictive Querying for Autoregressive Neural Sequence Models http://arxiv.org/pdf/2210.06416v1 Quantifying Uncertainty with Probabilistic Machine Learning Modeling in Wireless Sensing http://arxiv.org/pdf/2210.06399v1 DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System http://arxiv.org/pdf/2210.06310v1 Determining band structure parameters of two-dimensional materials by deep learning http://arxiv.org/pdf/2210.06302v1 Maximum entropy exploration in contextual bandits with neural networks and energy based models http://arxiv.org/pdf/2210.06225v1 On the Generalizability of ECG-based Stress Detection Models http://arxiv.org/pdf/2210.06213v1 Probabilistic Inverse Modeling: An Application in Hydrology http://arxiv.org/pdf/2210.06032v2 Modular Flows: Differential Molecular Generation http://arxiv.org/pdf/2210.05876v1 Statistical Modeling of Soft Error Influence on Neural Networks http://arxiv.org/pdf/2210.06373v1 Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching http://arxiv.org/pdf/2210.06272v1 Deep Koopman Representation of Nonlinear Time Varying Systems http://arxiv.org/pdf/2210.06071v1 Self-Validated Physics-Embedding Network: A General Framework for Inverse Modelling http://arxiv.org/pdf/2210.06269v1 Optimal Control of Transient Flows in Pipeline Networks with Heterogeneous Mixtures of Hydrogen and Natural Gas ./Link/2022-10-11 http://arxiv.org/pdf/2210.05599v1 Improving Sample Efficiency of Deep Learning Models in Electricity Market http://arxiv.org/pdf/2210.05577v1 What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness? http://arxiv.org/pdf/2210.05495v1 MAgNet: Mesh Agnostic Neural PDE Solver http://arxiv.org/pdf/2210.05443v1 QuCNN : A Quantum Convolutional Neural Network with Entanglement Based Backpropagation http://arxiv.org/pdf/2210.05337v1 SGD with large step sizes learns sparse features http://arxiv.org/pdf/2210.05320v1 Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning http://arxiv.org/pdf/2210.05189v1 Neural Networks are Decision Trees http://arxiv.org/pdf/2210.05111v1 Deep learning model compression using network sensitivity and gradients http://arxiv.org/pdf/2210.05087v1 Approximation of nearly-periodic symplectic maps via structure-preserving neural networks http://arxiv.org/pdf/2210.05448v1 A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning http://arxiv.org/pdf/2210.05547v1 Development of a high-fidelity computational tool for chemically reacting hypersonic flow simulations http://arxiv.org/pdf/2210.05489v1 Generating Approximate Ground States of Molecules Using Quantum Machine Learning ./Link/2022-10-10 http://arxiv.org/pdf/2210.04882v1 Layer Ensembles http://arxiv.org/pdf/2210.04860v1 Second-order regression models exhibit progressive sharpening to the edge of stability http://arxiv.org/pdf/2210.04763v1 On the Forward Invariance of Neural ODEs http://arxiv.org/pdf/2210.04520v1 Continual task learning in natural and artificial agents http://arxiv.org/pdf/2210.04431v1 Scientific Machine Learning for Modeling and Simulating Complex Fluids http://arxiv.org/pdf/2210.04371v1 A Detailed Study of Interpretability of Deep Neural Network based Top Taggers http://arxiv.org/pdf/2210.04349v1 Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network http://arxiv.org/pdf/2210.04338v1 A Method for Computing Inverse Parametric PDE Problems with Random-Weight Neural Networks http://arxiv.org/pdf/2210.04248v1 Residual Neural Networks for the Prediction of Planetary Collision Outcomes http://arxiv.org/pdf/2210.04209v1 Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning http://arxiv.org/pdf/2210.04193v1 Quasi-Monolithic Graph Neural Network for Fluid-Structure Interaction http://arxiv.org/pdf/2210.04165v1 Neural Extended Kalman Filters for Learning and Predicting Dynamics of Structural Systems http://arxiv.org/pdf/2210.04124v1 Generalized energy and gradient flow via graph framelets http://arxiv.org/pdf/2210.04083v1 Unified Probabilistic Neural Architecture and Weight Ensembling Improves Model Robustness http://arxiv.org/pdf/2210.04839v1 Benchmarking Reinforcement Learning Techniques for Autonomous Navigation http://arxiv.org/pdf/2210.04856v1 A Posteriori Error Estimate and Adaptivity for QM/MM Models of Crystalline Defects http://arxiv.org/pdf/2210.03881v1 Fourier Neural Solver for large sparse linear algebraic systems http://arxiv.org/pdf/2210.04792v1 Nonlinear Data-Driven Approximation of the Koopman Operator http://arxiv.org/pdf/2210.04849v1 Reconstructing velocity and pressure from sparse noisy particle tracks using Physics-Informed Neural Networks http://arxiv.org/pdf/2210.04259v1 Linear attention coupled Fourier neural operator for simulation of three-dimensional turbulence http://arxiv.org/pdf/2210.04225v1 Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials ./Link/2022-10-07 http://arxiv.org/pdf/2210.03728v1 Atomized Deep Learning Models http://arxiv.org/pdf/2210.03675v2 Koopman Neural Forecaster for Time Series with Temporal Distribution Shifts http://arxiv.org/pdf/2210.03590v1 Machine Learning Meets The Herbrand Universe http://arxiv.org/pdf/2210.03466v1 Latent Neural ODEs with Sparse Bayesian Multiple Shooting http://arxiv.org/pdf/2210.03426v1 Certified machine learning: Rigorous a posteriori error bounds for PDE defined PINNs http://arxiv.org/pdf/2210.03310v1 Scaling Forward Gradient With Local Losses http://arxiv.org/pdf/2210.03424v1 Kalman-Bucy-Informed Neural Network for System Identification http://arxiv.org/pdf/2210.03418v1 Data-driven probability density forecast for stochastic dynamical systems http://arxiv.org/pdf/2210.03389v1 Efficient Adaptive Stochastic Collocation Strategies for Advection-Diffusion Problems with Uncertain Inputs http://arxiv.org/pdf/2210.03314v1 Uniformly convex neural networks and non-stationary iterated network Tikhonov (iNETT) method ./Link/2022-10-06 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Document Annotation System for Scientific Knowledge Base Construction http://arxiv.org/pdf/2210.02637v1 IR2Net: Information Restriction and Information Recovery for Accurate Binary Neural Networks http://arxiv.org/pdf/2210.02700v1 Minimal-order Appointed-time Unknown Input Observers: Design and Applications http://arxiv.org/pdf/2210.03045v1 A neural network approach to high-dimensional optimal switching problems with jumps in energy markets http://arxiv.org/pdf/2210.02822v1 Solution of SAT Problems with the Adaptive-Bias Quantum Approximate Optimization Algorithm ./Link/2022-10-05 http://arxiv.org/pdf/2210.02349v1 Fitting a Directional Microstructure Model to Diffusion-Relaxation MRI Data with Self-Supervised Machine Learning http://arxiv.org/pdf/2210.02339v1 Particle clustering in turbulence: Prediction of spatial and statistical properties with deep learning http://arxiv.org/pdf/2210.02168v1 Bayesian Quadrature for Probability Threshold Robustness of Partially Undefined Functions 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Geometric Priors ./Link/2022-09-29 http://arxiv.org/pdf/2209.14977v1 Transformer Meets Boundary Value Inverse Problems http://arxiv.org/pdf/2209.14937v1 NAG-GS: Semi-Implicit, Accelerated and Robust Stochastic Optimizers http://arxiv.org/pdf/2209.14933v1 Training Normalizing Flows from Dependent Data http://arxiv.org/pdf/2209.14855v1 Continuous PDE Dynamics Forecasting with Implicit Neural Representations http://arxiv.org/pdf/2209.14781v1 Learning Parsimonious Dynamics for Generalization in Reinforcement Learning http://arxiv.org/pdf/2209.14733v1 Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights ./Link/2022-09-28 http://arxiv.org/pdf/2209.14267v1 Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets http://arxiv.org/pdf/2209.14115v1 Deep learning for gradient flows using the Brezis-Ekeland principle http://arxiv.org/pdf/2209.14089v1 Reinforcement Learning with Tensor Networks: Application to Dynamical Large Deviations 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Scaling for Neural Network Calibration http://arxiv.org/pdf/2209.11588v1 Learning Rigid Body Dynamics with Lagrangian Graph Neural Network http://arxiv.org/pdf/2209.11395v1 Achieve the Minimum Width of Neural Networks for Universal Approximation http://arxiv.org/pdf/2209.11366v1 A Jensen-Shannon Divergence Based Loss Function for Bayesian Neural Networks http://arxiv.org/pdf/2209.11355v1 Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body http://arxiv.org/pdf/2209.11677v1 PNeRF: Probabilistic Neural Scene Representations for Uncertain 3D Visual Mapping http://arxiv.org/pdf/2209.11655v1 Kernel-based quantum regressor models learn non-Markovianity ./Link/2022-09-22 http://arxiv.org/pdf/2209.11208v1 A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases http://arxiv.org/pdf/2209.10861v1 A novel corrective-source term approach to modeling unknown physics in aluminum extraction process http://arxiv.org/pdf/2209.10778v1 Nesting Forward Automatic Differentiation for Memory-Efficient Deep Neural Network Training http://arxiv.org/pdf/2209.10740v1 Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems http://arxiv.org/pdf/2209.10729v1 Fair Robust Active Learning by Joint Inconsistency http://arxiv.org/pdf/2209.10722v1 Enhanced Decentralized Federated Learning based on Consensus in Connected Vehicles http://arxiv.org/pdf/2209.11134v1 Power Method, Inverse Power Method and Shifted Inverse Power Method Neural Networks for Solving Eigenvalue Problems of Linear Operators http://arxiv.org/pdf/2209.11220v1 Quantum algorithms for uncertainty quantification: application to partial differential equations http://arxiv.org/pdf/2209.10870v1 On the sampling complexity of open quantum systems ./Link/2022-09-21 http://arxiv.org/pdf/2209.10428v1 An NWDAF Approach to 5G Core Network Signaling Traffic: Analysis and Characterization http://arxiv.org/pdf/2209.10222v1 Fairness Reprogramming 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deep learning architecture for solving stochastic differential equations http://arxiv.org/pdf/2209.09617v1 Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference http://arxiv.org/pdf/2209.09563v1 Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation http://arxiv.org/pdf/2209.09453v1 Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography http://arxiv.org/pdf/2209.09406v1 Probabilistic Generative Transformer Language models for Generative Design of Molecules ./Link/2022-09-19 http://arxiv.org/pdf/2209.08907v1 Learning Symbolic Model-Agnostic Loss Functions via Meta-Learning http://arxiv.org/pdf/2209.08906v1 A model-agnostic approach for generating Saliency Maps to explain inferred decisions of Deep Learning Models http://arxiv.org/pdf/2209.08750v1 Knowledge-based Analogical Reasoning in Neuro-symbolic Latent Spaces http://arxiv.org/pdf/2209.08739v1 Adaptive Multi-stage Density 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Bilevel Optimization Made Easy: A Simple First-Order Approach http://arxiv.org/pdf/2209.08660v1 Learn the Time to Learn: Replay Scheduling in Continual Learning http://arxiv.org/pdf/2209.08622v1 The Geometry of Self-supervised Learning Models and its Impact on Transfer Learning http://arxiv.org/pdf/2209.08595v1 Low-cost machine learning approach to the prediction of transition metal phosphor excited state properties http://arxiv.org/pdf/2209.08378v1 Inducing Early Neural Collapse in Deep Neural Networks for Improved Out-of-Distribution Detection http://arxiv.org/pdf/2209.08335v1 Efficient Deep Clustering of Human Activities and How to Improve Evaluation http://arxiv.org/pdf/2209.08307v1 A review of probabilistic forecasting and prediction with machine learning http://arxiv.org/pdf/2209.08206v1 Selective Token Generation for Few-shot Natural Language Generation http://arxiv.org/pdf/2209.08667v1 Semantic Segmentation using Neural Ordinary Differential Equations http://arxiv.org/pdf/2209.09205v1 Data-Driven Stochastic Optimal Control Using Kernel Gradients http://arxiv.org/pdf/2209.09170v1 Modified PSO based PID Sliding Mode Control using Improved Reaching Law for Nonlinear systems http://arxiv.org/pdf/2209.08458v1 Data-driven Step-to-step Dynamics based Adaptive Control for Robust and Versatile Underactuated Bipedal Robotic Walking http://arxiv.org/pdf/2209.08478v1 Time complexity analysis of quantum algorithms via linear representations for nonlinear ordinary and partial differential equations http://arxiv.org/pdf/2209.08995v1 Data-Driven Control of Stochastic Systems: An Innovation Estimation Approach http://arxiv.org/pdf/2209.08994v1 Optimal Controls for Forward-Backward Stochastic Differential Equations: Time-Inconsistency and Time-Consistent Solutions http://arxiv.org/pdf/2209.08637v1 Control-Consistent Learning of Koopman Embedding Models http://arxiv.org/pdf/2209.08729v1 Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood ./Link/2022-09-16 http://arxiv.org/pdf/2209.08005v1 Stability and Generalization for Markov Chain Stochastic Gradient Methods http://arxiv.org/pdf/2209.07921v1 ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery http://arxiv.org/pdf/2209.07805v1 A Comprehensive Benchmark for COVID-19 Predictive Modeling Using Electronic Health Records in Intensive Care: Choosing the Best Model for COVID-19 Prognosis http://arxiv.org/pdf/2209.07754v1 On the Robustness of Graph Neural Diffusion to Topology Perturbations http://arxiv.org/pdf/2209.07679v1 Learning Pair Potentials using Differentiable Simulations http://arxiv.org/pdf/2209.07919v1 iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking http://arxiv.org/pdf/2209.07685v1 Neural Koopman Control Barrier Functions for Safety-Critical Control of Unknown Nonlinear Systems http://arxiv.org/pdf/2209.07882v2 Solving Stochastic PDEs Using FEniCS and UQtk http://arxiv.org/pdf/2209.07714v1 Variational quantum algorithm for measurement extraction from the Navier-Stokes, Einstein, Maxwell, Boussniesq-type, Lin-Tsien, Camassa-Holm, Drinfeld-Sokolov-Wilson, and Hunter-Saxton equations http://arxiv.org/pdf/2209.08052v1 Autoregressive Transformers for Data-Driven Spatio-Temporal Learning of Turbulent Flows ./Link/2022-09-15 http://arxiv.org/pdf/2209.07521v1 On-Device Domain Generalization http://arxiv.org/pdf/2209.07413v1 Evolving Zero Cost Proxies For Neural Architecture Scoring http://arxiv.org/pdf/2209.07376v1 Understanding Deep Neural Function Approximation in Reinforcement Learning via $ε$-Greedy Exploration http://arxiv.org/pdf/2209.07116v1 Decentralized Learning with Separable Data: Generalization and Fast Algorithms http://arxiv.org/pdf/2209.07081v1 DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks http://arxiv.org/pdf/2209.07075v1 Bi-level Physics-Informed Neural Networks for PDE Constrained Optimization using Broyden's Hypergradients http://arxiv.org/pdf/2209.07067v1 Efficient learning of nonlinear prediction models with time-series privileged information http://arxiv.org/pdf/2209.07044v1 Fair Inference for Discrete Latent Variable Models http://arxiv.org/pdf/2209.07240v1 Neural Stochastic Control http://arxiv.org/pdf/2209.07320v1 Physically recurrent neural networks for path-dependent heterogeneous materials: embedding constitutive models in a data-driven surrogate ./Link/2022-09-14 http://arxiv.org/pdf/2209.06369v1 Data-Driven Machine Learning Models for a Multi-Objective Flapping Fin Unmanned Underwater Vehicle Control System http://arxiv.org/pdf/2209.06573v1 Using Spectral Submanifolds for Nonlinear Periodic Control http://arxiv.org/pdf/2209.06467v1 A deep learning energy-based method for classical elastoplasticity http://arxiv.org/pdf/2209.06558v1 Predicting molecular vibronic spectra using time-domain analog quantum simulation ./Link/2022-09-13 http://arxiv.org/pdf/2209.06095v1 A deep variational free energy approach to dense hydrogen http://arxiv.org/pdf/2209.05832v1 Sparse deep neural networks for modeling aluminum electrolysis dynamics http://arxiv.org/pdf/2209.05726v1 Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics http://arxiv.org/pdf/2209.05661v1 Unsupervised representational learning with recognition-parametrised probabilistic models http://arxiv.org/pdf/2209.05843v1 Continuous Design Control for Machine Learning in Certified Medical Systems http://arxiv.org/pdf/2209.06176v1 Generalized dimension truncation error analysis for high-dimensional numerical integration: lognormal setting and beyond http://arxiv.org/pdf/2209.06105v1 On floating point precision in computational fluid dynamics using OpenFOAM http://arxiv.org/pdf/2209.05711v1 Data reconstruction based on quantum neural networks ./Link/2022-09-12 http://arxiv.org/pdf/2209.05212v1 Amortised Inference in Structured Generative Models with Explaining Away http://arxiv.org/pdf/2209.04747v1 Diffusion Models in Vision: A Survey http://arxiv.org/pdf/2209.04726v1 Data-driven, multi-moment fluid modeling of Landau damping http://arxiv.org/pdf/2209.05082v1 Bayesian Learning for Disparity Map Refinement for Semi-Dense Active Stereo Vision http://arxiv.org/pdf/2209.05139v1 Automated MIMO Motion Feedforward Control: Efficient Learning through Data-Driven Gradients via Adjoint Experiments and Stochastic Approximation http://arxiv.org/pdf/2209.04997v1 Numerical approximation based on deep convolutional neural network for high-dimensional fully nonlinear merged PDEs and 2BSDEs http://arxiv.org/pdf/2209.04956v1 Simulation of open quantum system dynamics based on the generalized quantum master equation on quantum computing devices http://arxiv.org/pdf/2209.04955v1 An effective single molecule model that simulates dynamics of collective strong light-matter coupling ./Link/2022-09-09 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How to efficiently debug computational solid mechanics models so you can enjoy the beauty of simulations http://arxiv.org/pdf/2209.04078v1 Initial Value Problem Enhanced Sampling for Closed-Loop Optimal Control Design with Deep Neural Networks ./Link/2022-09-08 http://arxiv.org/pdf/2209.03933v1 NeuralFMU: Presenting a workflow for integrating hybrid NeuralODEs into real world applications http://arxiv.org/pdf/2209.03858v1 Simpler is better: Multilevel Abstraction with Graph Convolutional Recurrent Neural Network Cells for Traffic Prediction http://arxiv.org/pdf/2209.03855v1 SE(3)-DiffusionFields: Learning cost functions for joint grasp and motion optimization through diffusion http://arxiv.org/pdf/2209.03698v1 Incremental Correction in Dynamic Systems Modelled with Neural Networks for Constraint Satisfaction http://arxiv.org/pdf/2209.03695v1 Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes http://arxiv.org/pdf/2209.03668v1 Predict+Optimize for Packing and Covering LPs with Unknown Parameters in Constraints http://arxiv.org/pdf/2209.03525v1 Implicit Full Waveform Inversion with Deep Neural Representation ./Link/2022-09-07 http://arxiv.org/pdf/2209.03299v1 Geometric multimodal representation learning http://arxiv.org/pdf/2209.03276v1 Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks http://arxiv.org/pdf/2209.03210v1 Real-to-Sim: Deep Learning with Auto-Tuning to Predict Residual Errors using Sparse Data http://arxiv.org/pdf/2209.03171v1 Machine Learning Partners in Criminal Networks http://arxiv.org/pdf/2209.03003v1 Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow http://arxiv.org/pdf/2209.02977v1 Error Estimates and Physics Informed Augmentation of Neural Networks for Thermally Coupled Incompressible Navier Stokes Equations http://arxiv.org/pdf/2209.02880v1 Data Forecasts of the Epidemic COVID-19 by Deterministic and Stochastic Time-Dependent Models http://arxiv.org/pdf/2209.03290v1 Reconstruction of irregular flow dynamics around two square cylinders from sparse measurements using a data-driven algorithm http://arxiv.org/pdf/2209.03241v1 Dynamics with autoregressive neural quantum states: application to critical quench dynamics http://arxiv.org/pdf/2209.03202v1 Ab initio Quantum Simulation of Strongly Correlated Materials with Quantum Embedding ./Link/2022-09-06 http://arxiv.org/pdf/2209.02681v2 How important are activation functions in regression and classification? 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Decentralized Stochastic Optimization http://arxiv.org/pdf/2208.04319v1 PhyGNNet: Solving spatiotemporal PDEs with Physics-informed Graph Neural Network http://arxiv.org/pdf/2208.03680v1 Accelerating Numerical Solvers for Large-Scale Simulation of Dynamical System via NeurVec http://arxiv.org/pdf/2208.03635v1 Federated Adversarial Learning: A Framework with Convergence Analysis http://arxiv.org/pdf/2208.03616v1 Transmission Neural Networks: From Virus Spread Models to Neural Networks http://arxiv.org/pdf/2208.03498v2 Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies http://arxiv.org/pdf/2208.04530v1 VectorFlow: Combining Images and Vectors for Traffic Occupancy and Flow Prediction http://arxiv.org/pdf/2208.04283v1 LWGNet: Learned Wirtinger Gradients for Fourier Ptychographic Phase Retrieval http://arxiv.org/pdf/2208.03775v1 Video-based Human Action Recognition using Deep Learning: A Review http://arxiv.org/pdf/2208.05402v1 The Relationship Between Surface Pressure Spectra and Vorticity in a Turbulent Boundary Layer http://arxiv.org/pdf/2208.04280v1 Estimating density, velocity, and pressure fields in supersonic flow using physics-informed BOS http://arxiv.org/pdf/2208.03542v1 Velocity Reconstruction in Puffing Pool Fires with Physics-Informed Neural Networks ./Link/2022-08-05 http://arxiv.org/pdf/2208.03115v1 Multi-fidelity surrogate modeling using long short-term memory networks http://arxiv.org/pdf/2208.03113v1 On the non-universality of deep learning: quantifying the cost of symmetry http://arxiv.org/pdf/2208.03190v1 Reduced-order modeling for stochastic large-scale and time-dependent problems using deep spatial and temporal convolutional autoencoders http://arxiv.org/pdf/2208.03109v1 Mean flow data assimilation based on physics-informed neural networks http://arxiv.org/pdf/2208.03283v1 Hybrid Gate-Based and Annealing Quantum Computing for Large-Size Ising Problems ./Link/2022-08-04 http://arxiv.org/pdf/2208.02789v1 Feature 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Newton Conjugate Gradient Method on Multiple GPUs http://arxiv.org/pdf/2208.02007v1 Maintaining Performance with Less Data http://arxiv.org/pdf/2208.01913v1 EgPDE-Net: Building Continuous Neural Networks for Time Series Prediction with Exogenous Variables http://arxiv.org/pdf/2208.01841v1 Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data http://arxiv.org/pdf/2208.02104v1 Active Learning on a Programmable Photonic Quantum Processor http://arxiv.org/pdf/2208.02114v1 Solving Inverse PDE Problems using Grid-Free Monte Carlo Estimators ./Link/2022-08-02 http://arxiv.org/pdf/2208.01565v1 Approximate Bayesian Neural Operators: Uncertainty Quantification for Parametric PDEs http://arxiv.org/pdf/2208.01462v1 Physics-informed Deep Super-resolution for Spatiotemporal Data http://arxiv.org/pdf/2208.01358v1 What can we Learn by Predicting Accuracy? http://arxiv.org/pdf/2208.01204v1 Making a Spiking Net Work: Robust brain-like unsupervised machine learning http://arxiv.org/pdf/2208.01440v1 Viskositas: Viscosity Prediction of Multicomponent Chemical Systems ./Link/2022-08-01 http://arxiv.org/pdf/2208.00971v1 Probabilistic forecasts of extreme heatwaves using convolutional neural networks in a regime of lack of data http://arxiv.org/pdf/2208.00953v1 What do Deep Neural Networks Learn in Medical Images? http://arxiv.org/pdf/2208.00800v1 GANDSE: Generative Adversarial Network based Design Space Exploration for Neural Network Accelerator Design http://arxiv.org/pdf/2208.00564v1 Quantum Adaptive Fourier Features for Neural Density Estimation http://arxiv.org/pdf/2208.00331v1 CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks http://arxiv.org/pdf/2208.00246v1 Geometric deep learning for computational mechanics Part II: Graph embedding for interpretable multiscale plasticity http://arxiv.org/pdf/2208.00203v1 Adding Context to Source Code Representations for Deep Learning http://arxiv.org/pdf/2208.00491v1 Assimilation of wall-pressure measurements in high-speed flow over a cone http://arxiv.org/pdf/2208.00256v1 Machine and quantum learning for diamond-based quantum applications ./Link/2022-07-29 http://arxiv.org/pdf/2207.14742v1 Graph Neural Networks for Channel Decoding http://arxiv.org/pdf/2207.14694v1 Design Methodology for Deep Out-of-Distribution Detectors in Real-Time Cyber-Physical Systems http://arxiv.org/pdf/2207.14620v1 Computational complexity reduction of deep neural networks http://arxiv.org/pdf/2207.14554v1 Reweighted Manifold Learning of Collective Variables from Enhanced Sampling Simulations http://arxiv.org/pdf/2207.14443v1 A Survey of Learning on Small Data http://arxiv.org/pdf/2207.14419v1 Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions http://arxiv.org/pdf/2207.14668v1 lifex: a flexible, high performance library for the numerical solution of complex finite element problems 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http://arxiv.org/pdf/2207.13266v1 Sparse Deep Neural Network for Nonlinear Partial Differential Equations http://arxiv.org/pdf/2207.13380v1 Bridging Traditional and Machine Learning-based Algorithms for Solving PDEs: The Random Feature Method http://arxiv.org/pdf/2207.13449v1 Characterization of $F$-concavity preserved by the Dirichlet heat flow http://arxiv.org/pdf/2207.13554v1 Data-Driven Sample Average Approximation with Covariate Information ./Link/2022-07-26 http://arxiv.org/pdf/2207.12877v1 Representing Random Utility Choice Models with Neural Networks http://arxiv.org/pdf/2207.12800v1 PIXEL: Physics-Informed Cell Representations for Fast and Accurate PDE Solvers http://arxiv.org/pdf/2207.12409v1 Automated discovery of interpretable gravitational-wave population models http://arxiv.org/pdf/2207.12283v1 MedML: Fusing Medical Knowledge and Machine Learning Models for Early Pediatric COVID-19 Hospitalization and Severity Prediction http://arxiv.org/pdf/2207.11255v1 Learning Relaxation for Multigrid http://arxiv.org/pdf/2207.12067v1 Homomorphism Autoencoder -- Learning Group Structured Representations from Observed Transitions http://arxiv.org/pdf/2207.12051v1 Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks http://arxiv.org/pdf/2207.11842v1 $\textit{FastSVD-ML-ROM}$: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications http://arxiv.org/pdf/2207.12932v1 Hyperdimensional Computing vs. Neural Networks: Comparing Architecture and Learning Process http://arxiv.org/pdf/2207.11812v1 Federated Graph Machine Learning: A Survey of Concepts, Techniques, and Applications http://arxiv.org/pdf/2207.11742v1 From Multi-label Learning to Cross-Domain Transfer: A Model-Agnostic Approach http://arxiv.org/pdf/2207.11735v1 AMS-Net: Adaptive Multiscale Sparse Neural Network with Interpretable Basis Expansion for Multiphase Flow Problems http://arxiv.org/pdf/2207.11727v1 Can we achieve robustness from 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Quantum Machine Learning http://arxiv.org/pdf/2207.11049v1 Context-aware controller inference for stabilizing dynamical systems from scarce data http://arxiv.org/pdf/2207.10951v1 Hyper-Representations for Pre-Training and Transfer Learning http://arxiv.org/pdf/2207.10840v1 Robust and Safe Autonomous Navigation for Systems with Learned SE(3) Hamiltonian Dynamics ./Link/2022-07-21 http://arxiv.org/pdf/2207.10442v1 Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks http://arxiv.org/pdf/2207.10430v1 The Neural Race Reduction: Dynamics of Abstraction in Gated Networks http://arxiv.org/pdf/2207.10241v1 Unsupervised Legendre-Galerkin Neural Network for Stiff Partial Differential Equations http://arxiv.org/pdf/2207.10562v1 CheckINN: Wide Range Neural Network Verification in Imandra http://arxiv.org/pdf/2207.10435v1 Human Trajectory Prediction via Neural Social Physics http://arxiv.org/pdf/2207.10290v1 AugRmixAT: A Data Processing and Training Method for Improving Multiple Robustness and Generalization Performance http://arxiv.org/pdf/2207.10531v1 Hybrid Data-Driven Closure Strategies for Reduced Order Modeling http://arxiv.org/pdf/2207.10358v1 Domain Decomposition Learning Methods for Solving Elliptic Problems ./Link/2022-07-20 http://arxiv.org/pdf/2207.10046v1 Adaptive Step-Size Methods for Compressed SGD http://arxiv.org/pdf/2207.09971v1 NeuralNEB -- Neural Networks can find Reaction Paths Fast http://arxiv.org/pdf/2207.09955v1 Operation-Level Performance Benchmarking of Graph Neural Networks for Scientific Applications http://arxiv.org/pdf/2207.09849v1 Automated machine learning for borehole resistivity measurements http://arxiv.org/pdf/2207.09611v1 Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions http://arxiv.org/pdf/2207.09511v1 Approximation Power of Deep Neural Networks: an explanatory mathematical survey http://arxiv.org/pdf/2207.09370v2 Data-Centric Epidemic Forecasting: A Survey http://arxiv.org/pdf/2207.09344v1 Online Dynamics Learning for Predictive Control with an Application to Aerial Robots http://arxiv.org/pdf/2207.09141v1 Using Neural Networks by Modelling Semi-Active Shock Absorber http://arxiv.org/pdf/2207.09060v1 Data Science and Machine Learning in Education http://arxiv.org/pdf/2207.09677v1 Mathematical and numerical analysis to shrinking-dimer saddle dynamics with local Lipschitz conditions http://arxiv.org/pdf/2207.09618v1 Dynamical system-based computational models for solving combinatorial optimization on hypergraphs http://arxiv.org/pdf/2207.09850v1 Lock-in effect of over-tip shock waves and identification of the escaping vortex-shedding mode in pressure-driven tip leakage flow ./Link/2022-07-19 http://arxiv.org/pdf/2207.09299v1 Data-driven initialization of deep learning solvers for Hamilton-Jacobi-Bellman PDEs http://arxiv.org/pdf/2207.09220v1 Grad's Distribution Function for 13 Moments based Moment Gas Kinetic Solver for Steady and Unsteady Rarefied flows: Discrete and Explicit Forms http://arxiv.org/pdf/2207.08888v1 Point-particle drag, lift, and torque closure models using machine learning: hierarchical approach and interpretability http://arxiv.org/pdf/2207.09056v1 Learning quantum dissipation by the neural ordinary differential equation http://arxiv.org/pdf/2207.08394v1 A Simulation Methodology for Superconducting Qubit Readout Fidelity http://arxiv.org/pdf/2207.08977v1 Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift ./Link/2022-07-18 http://arxiv.org/pdf/2207.08675v1 Learning differentiable solvers for systems with hard constraints http://arxiv.org/pdf/2207.08597v1 FunQG: Molecular Representation Learning Via Quotient Graphs http://arxiv.org/pdf/2207.08483v1 wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws http://arxiv.org/pdf/2207.08457v1 A 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Modeling High Dimensional Monitored Structures http://arxiv.org/pdf/2207.07859v1 Deep Learning and Its Applications to WiFi Human Sensing: A Benchmark and A Tutorial http://arxiv.org/pdf/2207.07920v1 Physics Embedded Neural Network Vehicle Model and Applications in Risk-Aware Autonomous Driving Using Latent Features http://arxiv.org/pdf/2207.08596v1 Data-driven Self-triggered Control via Trajectory Prediction http://arxiv.org/pdf/2207.08370v1 Modeling and Control of Multi-Energy Dynamical Systems: Hidden Paths to Decarbonization http://arxiv.org/pdf/2207.08135v1 Parallelizing Explicit and Implicit Extrapolation Methods for Ordinary Differential Equations ./Link/2022-07-15 http://arxiv.org/pdf/2207.07475v1 Stable Invariant Models via Koopman Spectra ./Link/2022-07-14 http://arxiv.org/pdf/2207.06741v1 Differentiable Logics for Neural Network Training and Verification http://arxiv.org/pdf/2207.06680v1 Equivariant Hypergraph Diffusion Neural Operators http://arxiv.org/pdf/2207.06678v1 Deep 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Vision-Language Pre-training Models http://arxiv.org/pdf/2207.00874v1 Neural Networks for Path Planning http://arxiv.org/pdf/2207.01466v1 Physics-informed compressed sensing for PC-MRI: an inverse Navier-Stokes problem http://arxiv.org/pdf/2207.01114v1 Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems http://arxiv.org/pdf/2207.00936v1 Fast sparse flow field prediction around airfoils via multi-head perceptron based deep learning architecture ./Link/2022-07-01 http://arxiv.org/pdf/2207.00556v1 Learning to correct spectral methods for simulating turbulent flows http://arxiv.org/pdf/2207.00529v1 Deep Learning and Symbolic Regression for Discovering Parametric Equations http://arxiv.org/pdf/2207.00521v1 Using Machine Learning to Anticipate Tipping Points and Extrapolate to Post-Tipping Dynamics of Non-Stationary Dynamical Systems http://arxiv.org/pdf/2207.00391v1 Characterizing the Effect of Class Imbalance on the Learning Dynamics http://arxiv.org/pdf/2207.00389v1 Analysis of Kinetic Models for Label Switching and Stochastic Gradient Descent http://arxiv.org/pdf/2207.00377v1 Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs http://arxiv.org/pdf/2207.00300v1 Robust Bayesian Learning for Reliable Wireless AI: Framework and Applications http://arxiv.org/pdf/2207.00460v1 Exploring the solution space of linear inverse problems with GAN latent geometry http://arxiv.org/pdf/2207.00324v1 A data-driven approach to viscous fluid mechanics -- the stationary case ./Link/2022-06-30 http://arxiv.org/pdf/2206.15463v1 QUIDAM: A Framework for Quantization-Aware DNN Accelerator and Model Co-Exploration http://arxiv.org/pdf/2206.15457v1 PhySRNet: Physics informed super-resolution network for application in computational solid mechanics http://arxiv.org/pdf/2206.15448v1 Learning Iterative Reasoning through Energy Minimization http://arxiv.org/pdf/2206.15306v1 Transfer Learning with Deep Tabular Models http://arxiv.org/pdf/2206.15303v1 Physics-informed machine learning for Structural Health Monitoring http://arxiv.org/pdf/2206.15241v1 Benchmark Dataset for Precipitation Forecasting by Post-Processing the Numerical Weather Prediction http://arxiv.org/pdf/2206.15144v1 Neural Networks can Learn Representations with Gradient Descent http://arxiv.org/pdf/2206.15274v1 Exposing and addressing the fragility of neural networks in digital pathology http://arxiv.org/pdf/2206.15449v1 Neural network enhanced measurement efficiency for molecular groundstates http://arxiv.org/pdf/2206.15127v1 Experimental quantum simulation of non-Hermitian dynamical topological states using stochastic Schrödinger equation ./Link/2022-06-29 http://arxiv.org/pdf/2206.14697v1 Hidden Parameter Recurrent State Space Models For Changing Dynamics Scenarios http://arxiv.org/pdf/2206.14687v1 Multi-scale Physical Representations for Approximating PDE Solutions with Graph Neural Operators http://arxiv.org/pdf/2206.14666v1 Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning http://arxiv.org/pdf/2206.14476v1 Can Push-forward Generative Models Fit Multimodal Distributions? http://arxiv.org/pdf/2206.14337v1 Deformable Graph Transformer http://arxiv.org/pdf/2206.14420v1 Robust optimization for quantum reinforcement learning control using partial observations http://arxiv.org/pdf/2206.14288v1 Learning Time Delay Systems with Neural Ordinary Differential Equations ./Link/2022-06-28 http://arxiv.org/pdf/2206.14184v1 Integral Transforms in a Physics-Informed (Quantum) Neural Network setting: Applications & Use-Cases http://arxiv.org/pdf/2206.14115v1 Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization http://arxiv.org/pdf/2206.14092v1 Learning the Solution Operator of Boundary Value Problems using Graph Neural Networks http://arxiv.org/pdf/2206.14085v1 Continual Learning with Transformers for Image Classification http://arxiv.org/pdf/2206.13669v1 Studying Generalization Through Data Averaging http://arxiv.org/pdf/2206.13585v1 Heterogeneous mixtures of dictionary functions to approximate subspace invariance in Koopman operators http://arxiv.org/pdf/2206.13578v1 Materials Transformers Language Models for Generative Materials Design: a benchmark study http://arxiv.org/pdf/2206.13452v1 Causal Dynamics Learning for Task-Independent State Abstraction http://arxiv.org/pdf/2206.13336v1 Continual Learning of Dynamical Systems with Competitive Federated Reservoir Computing http://arxiv.org/pdf/2206.13211v1 Cracking nuts with a sledgehammer: when modern graph neural networks do worse than classical greedy algorithms http://arxiv.org/pdf/2206.13103v1 A mixed formulation for physics-informed neural networks as a potential solver for engineering problems in heterogeneous domains: comparison with finite element method http://arxiv.org/pdf/2206.12928v1 Learning neural state-space models: do we need a state estimator? http://arxiv.org/pdf/2206.12901v1 Noise-aware Physics-informed Machine Learning for Robust PDE Discovery http://arxiv.org/pdf/2206.12746v1 Modeling Oceanic Variables with Dynamic Graph Neural Networks http://arxiv.org/pdf/2206.12625v1 Asymptotic-Preserving Neural Networks for multiscale hyperbolic models of epidemic spread http://arxiv.org/pdf/2206.12569v1 Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels http://arxiv.org/pdf/2206.12520v2 Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks http://arxiv.org/pdf/2206.12800v1 Unified Energy Circuit-based Integrated Energy Management System: Theory, Implementation, and Application http://arxiv.org/pdf/2206.12799v1 An Efficient Optimal Energy Flow Model for Integrated Energy Systems Based on Energy Circuit Modeling in the Frequency Domain http://arxiv.org/pdf/2206.12698v1 Render unto Numerics : Orthogonal Polynomial Neural Operator for PDEs with Non-periodic Boundary Conditions http://arxiv.org/pdf/2206.13659v1 Data Assimilation in Operator Algebras http://arxiv.org/pdf/2206.13205v1 Multiscale model reduction for incompressible flows ./Link/2022-06-24 http://arxiv.org/pdf/2206.12342v1 HANF: Hyperparameter And Neural Architecture Search in Federated Learning http://arxiv.org/pdf/2206.12325v1 ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias http://arxiv.org/pdf/2206.12314v1 Learning sparse features can lead to overfitting in neural networks http://arxiv.org/pdf/2206.12201v1 Experimental graybox quantum control http://arxiv.org/pdf/2206.12391v1 Explicit Exactly Energy-conserving Methods for Hamiltonian Systems ./Link/2022-06-23 http://arxiv.org/pdf/2206.11795v1 Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos http://arxiv.org/pdf/2206.11740v1 Classical surrogates for quantum learning models http://arxiv.org/pdf/2206.11533v1 Stochastic Langevin Differential Inclusions with Applications to Machine Learning http://arxiv.org/pdf/2206.11801v1 A Database for Reduced-Complexity Modeling of Fluid Flows http://arxiv.org/pdf/2206.11800v1 Lagrangian stretching reveals stress topology in viscoelastic flows http://arxiv.org/pdf/2206.11772v1 Double-bracket flow quantum algorithm for diagonalization ./Link/2022-06-22 http://arxiv.org/pdf/2206.11120v1 Near-optimal control of dynamical systems with neural ordinary differential equations http://arxiv.org/pdf/2206.11030v1 KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images http://arxiv.org/pdf/2206.10991v1 Graph Neural Networks as Gradient Flows http://arxiv.org/pdf/2206.10897v1 How to Combine Variational Bayesian Networks in Federated Learning http://arxiv.org/pdf/2206.10844v1 Quantization Robust Federated Learning for Efficient Inference on Heterogeneous Devices http://arxiv.org/pdf/2206.10745v1 Derivate Informed Neural Operator: An Efficient Framework for High-Dimensional Parametric Derivative Learning http://arxiv.org/pdf/2206.10718v1 Physics-informed machine learning with differentiable programming for heterogeneous underground reservoir pressure management http://arxiv.org/pdf/2206.10654v1 On the Maximum Hessian Eigenvalue and Generalization http://arxiv.org/pdf/2206.10588v1 Robust SDE-Based Variational Formulations for Solving Linear PDEs via Deep Learning http://arxiv.org/pdf/2206.10586v1 D-CIPHER: Discovery of Closed-form PDEs http://arxiv.org/pdf/2206.10579v1 Gradient-Enhanced Physics-Informed Neural Networks for Power Systems Operational Support http://arxiv.org/pdf/2206.10524v1 Lyapunov Density Models: Constraining Distribution Shift in Learning-Based Control http://arxiv.org/pdf/2206.10480v2 Learning to Estimate and Refine Fluid Motion with Physical Dynamics http://arxiv.org/pdf/2206.10235v1 Riemannian data-dependent randomized smoothing for neural networks certification http://arxiv.org/pdf/2206.10188v1 Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition http://arxiv.org/pdf/2206.11038v1 Deep Reinforcement Learning for Turbulence Modeling in Large Eddy Simulations http://arxiv.org/pdf/2206.10121v1 Finite Expression Method for Solving High-Dimensional Partial Differential Equations http://arxiv.org/pdf/2206.10093v1 DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials http://arxiv.org/pdf/2206.10034v1 Deep Learning Models on CPUs: A Methodology for Efficient Training http://arxiv.org/pdf/2206.10014v1 Deep Partial Least Squares for Empirical Asset Pricing http://arxiv.org/pdf/2206.09992v1 Hyperparameter Importance of Quantum Neural Networks Across Small Datasets http://arxiv.org/pdf/2206.09961v1 Critical Investigation of Failure Modes in Physics-informed Neural Networks http://arxiv.org/pdf/2206.09909v1 Low-Precision Stochastic Gradient Langevin Dynamics http://arxiv.org/pdf/2206.09872v1 A Neural Network Based Method with Transfer Learning for Genetic Data Analysis http://arxiv.org/pdf/2206.09798v1 Actively Learning Deep Neural Networks with Uncertainty Sampling Based on Sum-Product Networks http://arxiv.org/pdf/2206.09654v1 Performance Prediction in Major League Baseball by Long Short-Term Memory Networks http://arxiv.org/pdf/2206.09513v2 $C^*$-algebra Net: A New Approach Generalizing Neural Network Parameters to $C^*$-algebra http://arxiv.org/pdf/2206.09379v1 0/1 Deep Neural Networks via Block Coordinate Descent http://arxiv.org/pdf/2206.09349v1 Quantifying Uncertainty In Traffic State Estimation Using Generative Adversarial Networks http://arxiv.org/pdf/2206.09321v1 Mitigating Learning Complexity in Physics and Equality Constrained Artificial Neural Networks http://arxiv.org/pdf/2206.09319v1 TrafficFlowGAN: Physics-informed Flow based Generative Adversarial Network for Uncertainty Quantification http://arxiv.org/pdf/2206.09299v1 Enforcing Continuous Physical Symmetries in Deep Learning Network for Solving Partial Differential Equations http://arxiv.org/pdf/2206.09247v1 Reduced Robust Random Cut Forest for Out-Of-Distribution detection in machine learning models http://arxiv.org/pdf/2206.09241v1 An Empirical Study of Quantum Dynamics as a Ground State Problem with Neural Quantum States http://arxiv.org/pdf/2206.10526v1 QuantFace: Towards Lightweight Face Recognition by Synthetic Data Low-bit Quantization http://arxiv.org/pdf/2206.10290v1 Discretization and index-robust error analysis for constrained high-index saddle dynamics on high-dimensional sphere http://arxiv.org/pdf/2206.10061v1 Improving numerical accuracy for the viscous-plastic formulation of sea ice http://arxiv.org/pdf/2206.09942v1 A globally convergent method to accelerate large-scale optimization using on-the-fly model hyperreduction: application to shape optimization http://arxiv.org/pdf/2206.09527v1 Simultaneous approximation of a smooth function and its derivatives by deep neural networks with piecewise-polynomial activations http://arxiv.org/pdf/2206.09955v1 The Sparse-Grid-Based Adaptive Spectral Koopman Method http://arxiv.org/pdf/2206.09571v1 Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations http://arxiv.org/pdf/2206.10576v1 How viable is quantum annealing for solving linear algebra problems? http://arxiv.org/pdf/2206.10502v1 Equation-of-motion variational quantum eigensolver method for computing molecular excitation energies, ionization potentials, and electron affinities http://arxiv.org/pdf/2206.09966v1 Meta-Learning Digitized-Counterdiabatic Quantum Optimization http://arxiv.org/pdf/2206.09908v1 Learning Optimal Flows for Non-Equilibrium Importance Sampling ./Link/2022-06-17 http://arxiv.org/pdf/2206.08885v1 Incorporating intratumoral heterogeneity into weakly-supervised deep learning models via variance pooling http://arxiv.org/pdf/2206.08809v1 Holistic Transformer: A Joint Neural Network for Trajectory Prediction and Decision-Making of Autonomous Vehicles http://arxiv.org/pdf/2206.08702v1 Sheaf Neural Networks with Connection Laplacians http://arxiv.org/pdf/2206.08656v1 tinySNN: Towards Memory- and Energy-Efficient Spiking Neural Networks http://arxiv.org/pdf/2206.08598v1 On the Influence of Enforcing Model Identifiability on Learning dynamics of Gaussian Mixture Models http://arxiv.org/pdf/2206.08594v1 Accelerating numerical methods by gradient-based meta-solving http://arxiv.org/pdf/2206.08492v1 TKIL: Tangent Kernel Approach for Class Balanced Incremental Learning http://arxiv.org/pdf/2206.08489v1 Debugging using Orthogonal Gradient Descent http://arxiv.org/pdf/2206.08825v1 An integral equation method for the advection-diffusion equation on time-dependent domains in the plane http://arxiv.org/pdf/2206.08658v1 Physics Informed Neural Networks for Two Dimensional Incompressible Thermal Convection Problems ./Link/2022-06-16 http://arxiv.org/pdf/2206.08297v1 GoodBye WaveNet -- A Language Model for Raw Audio with Context of 1/2 Million Samples http://arxiv.org/pdf/2206.08201v1 Learning Physics between Digital Twins with Low-Fidelity Models and Physics-Informed Gaussian Processes http://arxiv.org/pdf/2206.07932v1 Lifelong Wandering: A realistic few-shot online continual learning setting http://arxiv.org/pdf/2206.07875v1 Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training ./Link/2022-06-15 http://arxiv.org/pdf/2206.07707v1 Variable Bitrate Neural Fields http://arxiv.org/pdf/2206.07697v1 MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields http://arxiv.org/pdf/2206.07681v1 Learning to Accelerate Partial Differential Equations via Latent Global Evolution http://arxiv.org/pdf/2206.07680v1 Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator http://arxiv.org/pdf/2206.07609v1 Epistemic Deep Learning http://arxiv.org/pdf/2206.07579v1 A Comprehensive Survey on Deep Clustering: Taxonomy, Challenges, and Future Directions http://arxiv.org/pdf/2206.07562v1 Bayesian Federated Learning via Predictive Distribution Distillation http://arxiv.org/pdf/2206.07335v1 On Numerical Integration in Neural Ordinary Differential Equations http://arxiv.org/pdf/2206.07311v1 Can pruning improve certified robustness of neural networks? http://arxiv.org/pdf/2206.07558v1 Contextualization and Generalization in Entity and Relation Extraction http://arxiv.org/pdf/2206.07534v1 Optimal Synthesis of LTI Koopman Models for Nonlinear Systems with Inputs http://arxiv.org/pdf/2206.07303v1 Energetic Variational Neural Network Discretizations to Gradient Flows http://arxiv.org/pdf/2206.07366v1 Tuneable Gaussian entanglement in levitated nanoparticle arrays ./Link/2022-06-14 http://arxiv.org/pdf/2206.06817v1 Physics-Informed Transfer Learning Strategy to Accelerate Unsteady Fluid Flow Simulations http://arxiv.org/pdf/2206.06577v1 Physics Informed Neural Fields for Smoke Reconstruction with Sparse Data http://arxiv.org/pdf/2206.06485v1 What Should I Know? Using Meta-gradient Descent for Predictive Feature Discovery in a Single Stream of Experience http://arxiv.org/pdf/2206.06476v1 Explainable Mixed Data Representation and Lossless Visualization Toolkit for Knowledge Discovery http://arxiv.org/pdf/2206.06422v1 Symbolic Regression in Materials Science: Discovering Interatomic Potentials from Data http://arxiv.org/pdf/2206.06317v1 Learning Uncertainty with Artificial Neural Networks for Improved Predictive Process Monitoring http://arxiv.org/pdf/2206.05916v1 Why Quantization Improves Generalization: NTK of Binary Weight Neural Networks http://arxiv.org/pdf/2206.05909v1 Local distance preserving auto-encoders using Continuous k-Nearest Neighbours graphs http://arxiv.org/pdf/2206.05846v1 InBiaseD: Inductive Bias Distillation to Improve Generalization and Robustness through Shape-awareness http://arxiv.org/pdf/2206.05794v1 SGD Noise and Implicit Low-Rank Bias in Deep Neural Networks http://arxiv.org/pdf/2206.05720v1 Machine learning based surrogate modeling with SVD enabled training for nonlinear civil structures subject to dynamic loading http://arxiv.org/pdf/2206.05655v1 Variational Bayes Deep Operator Network: A data-driven Bayesian solver for parametric differential equations http://arxiv.org/pdf/2206.05643v1 Density Regression and Uncertainty Quantification with Bayesian Deep Noise Neural Networks http://arxiv.org/pdf/2206.05617v1 Federated Learning with Research Prototypes for Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology http://arxiv.org/pdf/2206.05562v1 Parameter Convex Neural Networks http://arxiv.org/pdf/2206.05490v1 Discovery and density estimation of latent confounders in Bayesian networks with evidence lower bound http://arxiv.org/pdf/2206.05442v1 A Dataset and Benchmark for Automatically Answering and Generating Machine Learning Final Exams http://arxiv.org/pdf/2206.06882v1 An Accurate HDDL Domain Learning Algorithm from Partial and Noisy Observations http://arxiv.org/pdf/2206.06202v2 Constraint Guided Gradient Descent: Guided Training with Inequality Constraints http://arxiv.org/pdf/2206.05625v1 A Review on Plastic Artificial Neural Networks: Exploring the Intersection between Neural Architecture Search and Continual Learning http://arxiv.org/pdf/2206.05395v1 Why is constrained neural language generation particularly challenging? http://arxiv.org/pdf/2206.06715v1 Semi-signed neural fitting for surface reconstruction from unoriented point clouds http://arxiv.org/pdf/2206.06506v1 Spiking Neural Networks for Frame-based and Event-based Single Object Localization http://arxiv.org/pdf/2206.06664v1 Hybrid Projection Methods for Solution Decomposition in Large-scale Bayesian Inverse Problems http://arxiv.org/pdf/2206.06536v1 On Learning the Dynamical Response of Nonlinear Control Systems with Deep Operator Networks http://arxiv.org/pdf/2206.06451v1 The Kolmogorov Infinite Dimensional Equation in a Hilbert space Via Deep Learning Methods http://arxiv.org/pdf/2206.05577v1 Local Randomized Neural Networks with Discontinuous Galerkin Methods for Partial Differential Equations http://arxiv.org/pdf/2206.05531v1 A Novel Meshless Method Based on the Virtual Construction of Node Control Domains for Porous Flow Problems http://arxiv.org/pdf/2206.05508v1 Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing http://arxiv.org/pdf/2206.06733v1 Data-Driven Mirror Descent with Input-Convex Neural Networks http://arxiv.org/pdf/2206.06628v1 Improving control based importance sampling strategies for metastable diffusions via adapted metadynamics http://arxiv.org/pdf/2206.05907v1 Computational Models based on Synchronized Oscillators for Solving Combinatorial Optimization Problems http://arxiv.org/pdf/2206.06073v2 General Solution to 2D Steady Navier-Stokes Equation for Incompressible Flow without vorticity diffusion http://arxiv.org/pdf/2206.06287v1 Physics-informed neural networks for quantum control ./Link/2022-06-13 http://arxiv.org/pdf/2206.05589v1 Determinable and interpretable network representation for link prediction ./Link/2022-06-10 http://arxiv.org/pdf/2206.05239v1 StructCoder: Structure-Aware Transformer for Code Generation http://arxiv.org/pdf/2206.05194v1 Learning the Space of Deep Models http://arxiv.org/pdf/2206.05183v1 GD-VAEs: Geometric Dynamic Variational Autoencoders for Learning Nonlinear Dynamics and Dimension Reductions http://arxiv.org/pdf/2206.05077v1 Tensor Train for Global Optimization Problems in Robotics http://arxiv.org/pdf/2206.04976v1 Refining neural network predictions using background knowledge http://arxiv.org/pdf/2206.04872v1 Multi-fidelity Hierarchical Neural Processes http://arxiv.org/pdf/2206.04843v1 Neural Laplace: Learning diverse classes of differential equations in the Laplace domain http://arxiv.org/pdf/2206.04827v1 A Fast Spectral Solver for the Heat Equation, with Applications to Navier--Stokes http://arxiv.org/pdf/2206.05226v1 On the generalizability of data-driven turbulence closure models http://arxiv.org/pdf/2206.05198v1 Stochastic formulation of incompressible fluid flows in wall bounded regions ./Link/2022-06-09 http://arxiv.org/pdf/2206.04672v1 Overcoming the Spectral Bias of Neural Value Approximation http://arxiv.org/pdf/2206.04642v1 Probability flow solution of the Fokker-Planck equation http://arxiv.org/pdf/2206.04620v1 On the Generalization and Adaption Performance of Causal Models http://arxiv.org/pdf/2206.04594v1 Field Level Neural Network Emulator for Cosmological N-body Simulations http://arxiv.org/pdf/2206.04573v1 Simple lessons from complex learning: what a neural network model learns about cosmic structure formation http://arxiv.org/pdf/2206.04569v1 Benefits of Overparameterized Convolutional Residual Networks: Function Approximation under Smoothness Constraint http://arxiv.org/pdf/2206.04463v1 Meet You Halfway: Explaining Deep Learning Mysteries http://arxiv.org/pdf/2206.04406v1 Unsupervised Learning of the Total Variation Flow http://arxiv.org/pdf/2206.04285v1 Pseudo-Poincaré: A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks http://arxiv.org/pdf/2206.04373v1 Adiabatic quantum computing with parameterized quantum circuits ./Link/2022-06-08 http://arxiv.org/pdf/2206.03726v1 Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models http://arxiv.org/pdf/2206.03688v1 Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials http://arxiv.org/pdf/2206.03637v1 Exploring accurate potential energy surfaces via integrating variational quantum eigensovler with machine learning ./Link/2022-06-07 http://arxiv.org/pdf/2206.03483v1 Few-Shot Learning by Dimensionality Reduction in Gradient Space http://arxiv.org/pdf/2206.03466v1 Adversarial Reprogramming Revisited http://arxiv.org/pdf/2206.03451v1 Combining physics-based and data-driven techniques for reliable hybrid analysis and modeling using the corrective source term approach http://arxiv.org/pdf/2206.03314v1 Integrating Random Effects in Deep Neural Networks http://arxiv.org/pdf/2206.03305v1 Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking http://arxiv.org/pdf/2206.03304v1 On the balance between the training time and interpretability of neural ODE for time series modelling http://arxiv.org/pdf/2206.03299v1 Generalization Error Bounds for Deep Neural Networks Trained by SGD http://arxiv.org/pdf/2206.03293v1 Joint Manifold Learning and Density Estimation Using Normalizing Flows http://arxiv.org/pdf/2206.03198v1 Explaining the physics of transfer learning a data-driven subgrid-scale closure to a different turbulent flow http://arxiv.org/pdf/2206.03066v1 Recent Advances for Quantum Neural Networks in Generative Learning http://arxiv.org/pdf/2206.02972v1 Decomposed Linear Dynamical Systems (dLDS) for learning the latent components of neural dynamics http://arxiv.org/pdf/2206.02911v1 Boundary informed inverse PDE problems on discrete Riemann surfaces http://arxiv.org/pdf/2206.02889v1 Conditional Seq2Seq model for the time-dependent two-level system http://arxiv.org/pdf/2206.02886v1 Graph Rationalization with Environment-based Augmentations http://arxiv.org/pdf/2206.02819v1 Deep Learning Models of the Discrete Component of the Galactic Interstellar Gamma-Ray Emission http://arxiv.org/pdf/2206.02806v1 Quantum Neural Network Classifiers: A Tutorial http://arxiv.org/pdf/2206.02660v1 Port-Hamiltonian Neural Networks with State Dependent Ports http://arxiv.org/pdf/2206.02607v1 CROM: Continuous Reduced-Order Modeling of PDEs Using Implicit Neural Representations http://arxiv.org/pdf/2206.02535v1 Certified Robustness in Federated Learning http://arxiv.org/pdf/2206.02391v1 Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference http://arxiv.org/pdf/2206.03322v1 Deep Learning-based FEA surrogate for sub-sea pressure vessel http://arxiv.org/pdf/2206.02789v1 Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes http://arxiv.org/pdf/2206.02261v1 Towards Individual Grevy's Zebra Identification via Deep 3D Fitting and Metric Learning http://arxiv.org/pdf/2206.02218v1 Statistical Deep Learning for Spatial and Spatio-Temporal Data http://arxiv.org/pdf/2206.02196v1 Machine learning applications for electricity market agent-based models: A systematic literature review http://arxiv.org/pdf/2206.02183v1 Functional Ensemble Distillation http://arxiv.org/pdf/2206.02178v1 Factored Conditional Filtering: Tracking States and Estimating Parameters in High-Dimensional Spaces http://arxiv.org/pdf/2206.02786v1 Impossibility of Collective Intelligence http://arxiv.org/pdf/2206.02102v1 AUTM Flow: Atomic Unrestricted Time Machine for Monotonic Normalizing Flows http://arxiv.org/pdf/2206.02088v1 Inference for Interpretable Machine Learning: Fast, Model-Agnostic Confidence Intervals for Feature Importance http://arxiv.org/pdf/2206.03254v1 Demystifying the Global Convergence Puzzle of Learning Over-parameterized ReLU Nets in Very High Dimensions http://arxiv.org/pdf/2206.02785v1 Zeroth-Order SciML: Non-intrusive Integration of Scientific Software with Deep Learning http://arxiv.org/pdf/2206.02029v1 Guided Deep Metric Learning http://arxiv.org/pdf/2206.02016v1 Is $L^2$ Physics-Informed Loss Always Suitable for Training Physics-Informed Neural Network? http://arxiv.org/pdf/2206.01927v1 Variational Monte Carlo Approach to Partial Differential Equations with Neural Networks http://arxiv.org/pdf/2206.01913v1 Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees http://arxiv.org/pdf/2206.01861v1 ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers http://arxiv.org/pdf/2206.03179v1 TSFEDL: A Python Library for Time Series Spatio-Temporal Feature Extraction and Prediction using Deep Learning (with Appendices on Detailed Network Architectures and Experimental Cases of Study) http://arxiv.org/pdf/2206.02733v1 Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review http://arxiv.org/pdf/2206.02495v1 A Resource-efficient Spiking Neural Network Accelerator Supporting Emerging Neural Encoding http://arxiv.org/pdf/2206.02671v1 Canonical Cortical Graph Neural Networks and its Application for Speech Enhancement in Future Audio-Visual Hearing Aids http://arxiv.org/pdf/2206.02203v1 3D Convolutional with Attention for Action Recognition http://arxiv.org/pdf/2206.02880v1 A Learning- and Scenario-based MPC Design for Nonlinear Systems in LPV Framework with Safety and Stability Guarantees http://arxiv.org/pdf/2206.01866v1 Robust and Kernelized Data-Enabled Predictive Control for Nonlinear Systems http://arxiv.org/pdf/2206.03439v1 Solving Non-local Fokker-Planck Equations by Deep Learning 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physics-informed acoustic emission mapping http://arxiv.org/pdf/2206.01463v1 Safety Certification for Stochastic Systems via Neural Barrier Functions http://arxiv.org/pdf/2206.01541v1 A robust solution strategy for the Cahn-Larché equations http://arxiv.org/pdf/2206.01641v1 Large Eddy Simulations of bubbly flows and breaking waves with Smoothed Particle Hydrodynamics ./Link/2022-06-02 http://arxiv.org/pdf/2206.01085v1 Incorporating Explicit Uncertainty Estimates into Deep Offline Reinforcement Learning http://arxiv.org/pdf/2206.00939v1 Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs http://arxiv.org/pdf/2206.00885v1 Coordinated Double Machine Learning http://arxiv.org/pdf/2206.00860v1 Self-Consistency of the Fokker-Planck Equation http://arxiv.org/pdf/2206.00858v1 Bayesian Inference of Stochastic Dynamical Networks http://arxiv.org/pdf/2206.00853v1 Masked Bayesian Neural Networks : Computation and Optimality http://arxiv.org/pdf/2206.00807v1 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with physical outputs can approach state-of-the-art hydrologic prediction accuracy http://arxiv.org/pdf/2203.14500v1 MolGenSurvey: A Systematic Survey in Machine Learning Models for Molecule Design http://arxiv.org/pdf/2203.14495v1 Conjugate Gradient Method for Generative Adversarial Networks http://arxiv.org/pdf/2203.14396v1 Learned coupled inversion for carbon sequestration monitoring and forecasting with Fourier neural operators http://arxiv.org/pdf/2203.14386v1 Velocity continuation with Fourier neural operators for accelerated uncertainty quantification http://arxiv.org/pdf/2203.14177v1 Benchmarking Deep AUROC Optimization: Loss Functions and Algorithmic Choices http://arxiv.org/pdf/2203.13944v1 SolidGen: An Autoregressive Model for Direct B-rep Synthesis http://arxiv.org/pdf/2203.13965v1 Augmenting Knowledge Graphs for Better Link Prediction http://arxiv.org/pdf/2203.14408v1 Control-Oriented Modeling of Pipe Flow in Gas Processing Facilities http://arxiv.org/pdf/2203.14114v1 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Learning Traffic Flow Patterns Using a Graph Convolutional Neural Network http://arxiv.org/pdf/2202.10916v1 Remaining Useful Life Prediction Using Temporal Deep Degradation Network for Complex Machinery with Attention-based Feature Extraction http://arxiv.org/pdf/2202.10027v1 Toward more generalized Malicious URL Detection Models http://arxiv.org/pdf/2202.09977v1 RTGNN: A Novel Approach to Model Stochastic Traffic Dynamics http://arxiv.org/pdf/2202.09954v1 Theoretical Analysis of Deep Neural Networks in Physical Layer Communication http://arxiv.org/pdf/2202.09891v1 Equivariant Graph Attention Networks for Molecular Property Prediction http://arxiv.org/pdf/2202.09855v1 ChemTab: A Physics Guided Chemistry Modeling Framework http://arxiv.org/pdf/2202.11197v1 Real-time Over-the-air Adversarial Perturbations for Digital Communications using Deep Neural Networks http://arxiv.org/pdf/2202.09826v1 Efficient Continual Learning Ensembles in Neural Network Subspaces http://arxiv.org/pdf/2202.09664v1 Accurate Prediction and Uncertainty Estimation using Decoupled Prediction Interval Networks http://arxiv.org/pdf/2202.11200v1 Quantum Heterogeneous Distributed Deep Learning Architectures: Models, Discussions, and Applications http://arxiv.org/pdf/2202.09571v1 Bit-wise Training of Neural Network Weights http://arxiv.org/pdf/2202.10774v1 Social Computational Design Method for Generating Product Shapes with GAN and Transformer Models http://arxiv.org/pdf/2202.10335v1 Explainability in Machine Learning: a Pedagogical Perspective http://arxiv.org/pdf/2202.11762v1 Safe Control with Learned Certificates: A Survey of Neural Lyapunov, Barrier, and Contraction methods http://arxiv.org/pdf/2202.09712v1 Physics-informed neural networks for learning the homogenized coefficients of multiscale elliptic equations http://arxiv.org/pdf/2202.09488v1 Function-valued RKHS-based Operator Learning for Differential Equations http://arxiv.org/pdf/2202.10915v1 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Improving Neural Network Estimates with Concepts from Semiparametric Statistics http://arxiv.org/pdf/2202.09052v1 Tackling benign nonconvexity with smoothing and stochastic gradients http://arxiv.org/pdf/2202.08955v1 R2-D2: Repetitive Reprediction Deep Decipher for Semi-Supervised Deep Learning http://arxiv.org/pdf/2202.09009v1 LG-LSQ: Learned Gradient Linear Symmetric Quantization http://arxiv.org/pdf/2202.08991v1 Joint Learning of Frequency and Spatial Domains for Dense Predictions http://arxiv.org/pdf/2202.09281v1 Unsupervised and supervised learning of interacting topological phases from single-particle correlation functions ./Link/2022-02-17 http://arxiv.org/pdf/2202.08708v1 Learning stochastic dynamics and predicting emergent behavior using transformers http://arxiv.org/pdf/2202.08658v1 The merged-staircase property: a necessary and nearly sufficient condition for SGD learning of sparse functions on two-layer neural networks http://arxiv.org/pdf/2202.08494v1 Learning continuous 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http://arxiv.org/pdf/2202.07403v1 Deep learning and differential equations for modeling changes in individual-level latent dynamics between observation periods http://arxiv.org/pdf/2202.07242v1 Neural Architecture Search for Dense Prediction Tasks in Computer Vision http://arxiv.org/pdf/2202.07178v1 Federated Learning with Sparsified Model Perturbation: Improving Accuracy under Client-Level Differential Privacy http://arxiv.org/pdf/2202.07176v1 DeepONet-Grid-UQ: A Trustworthy Deep Operator Framework for Predicting the Power Grid's Post-Fault Trajectories http://arxiv.org/pdf/2202.07101v1 A Survey on Dynamic Neural Networks for Natural Language Processing http://arxiv.org/pdf/2202.07399v1 Interpreting a Machine Learning Model for Detecting Gravitational Waves http://arxiv.org/pdf/2202.07499v1 Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition ./Link/2022-02-14 http://arxiv.org/pdf/2202.06880v1 Black-Box Generalization http://arxiv.org/pdf/2202.06860v1 Physics-Informed Deep Monte Carlo Quantile Regression method for Interval Multilevel Bayesian Network-based Satellite Heat Reliability Analysis http://arxiv.org/pdf/2202.06596v1 Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction http://arxiv.org/pdf/2202.06533v1 An Introduction to Neural Data Compression http://arxiv.org/pdf/2202.06526v1 Benign Overfitting in Two-layer Convolutional Neural Networks http://arxiv.org/pdf/2202.06493v1 FLHub: a Federated Learning model sharing service http://arxiv.org/pdf/2202.06481v1 A Survey on Machine Learning Approaches for Modelling Intuitive Physics http://arxiv.org/pdf/2202.06453v1 Input-to-State Stable Neural Ordinary Differential Equations with Applications to Transient Modeling of Circuits http://arxiv.org/pdf/2202.06416v1 State-of-the-Art Review of Design of Experiments for Physics-Informed Deep Learning http://arxiv.org/pdf/2202.06170v1 An AI-based Domain-Decomposition Non-Intrusive Reduced-Order Model for Extended Domains applied to Multiphase Flow in Pipes http://arxiv.org/pdf/2202.06137v1 MIONet: Learning multiple-input operators via tensor product http://arxiv.org/pdf/2202.06036v1 Neural NID Rules http://arxiv.org/pdf/2202.05996v1 Coupling Online-Offline Learning for Multi-distributional Data Streams http://arxiv.org/pdf/2202.05994v1 Physics-Guided Problem Decomposition for Scaling Deep Learning of High-dimensional Eigen-Solvers: The Case of Schrödinger's Equation http://arxiv.org/pdf/2202.06902v1 A Multi-Fidelity Active Learning Method for Global Design Optimization Problems with Noisy Evaluations http://arxiv.org/pdf/2202.06804v1 Flexible learning of quantum states with generative query neural networks ./Link/2022-02-11 http://arxiv.org/pdf/2202.05750v1 Bounded nonlinear forecasts of partially observed geophysical systems with physics-constrained deep learning http://arxiv.org/pdf/2202.05714v1 Modeling Reservoir Release Using Pseudo-Prospective Learning and Physical Simulations to Predict Water Temperature http://arxiv.org/pdf/2202.05613v1 CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning http://arxiv.org/pdf/2202.05460v1 Reduced order modeling with Barlow Twins self-supervised learning: Navigating the space between linear and nonlinear solution manifolds http://arxiv.org/pdf/2202.05766v1 Learning via nonlinear conjugate gradients and depth-varying neural ODEs http://arxiv.org/pdf/2202.05476v1 Physics-Informed PointNet: A Deep Learning Solver for Steady-State Incompressible Flows and Thermal Fields on Multiple Sets of Irregular Geometries ./Link/2022-02-10 http://arxiv.org/pdf/2202.05258v1 Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks http://arxiv.org/pdf/2202.05254v1 Deep Learning in Random Neural Fields: Numerical Experiments via Neural Tangent Kernel http://arxiv.org/pdf/2202.05240v1 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descent: Machine learning for parametric PDEs and financial derivative pricing ./Link/2022-02-04 http://arxiv.org/pdf/2202.02249v1 Functional Mixtures-of-Experts http://arxiv.org/pdf/2202.02248v1 Backpropagation Neural Tree http://arxiv.org/pdf/2202.02003v1 Capturing and incorporating expert knowledge into machine learning models for quality prediction in manufacturing http://arxiv.org/pdf/2202.01958v1 Demystify Optimization and Generalization of Over-parameterized PAC-Bayesian Learning http://arxiv.org/pdf/2202.01954v1 Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems http://arxiv.org/pdf/2202.01944v1 Learning Representation from Neural Fisher Kernel with Low-rank Approximation http://arxiv.org/pdf/2202.01943v1 PSO-PINN: Physics-Informed Neural Networks Trained with Particle Swarm Optimization http://arxiv.org/pdf/2202.02188v1 Koopman von Neumann mechanics and the Koopman representation: A perspective on solving 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A one-hidden-layer theoretical analysis http://arxiv.org/pdf/2201.08506v1 alpha-Deep Probabilistic Inference (alpha-DPI): efficient uncertainty quantification from exoplanet astrometry to black hole feature extraction http://arxiv.org/pdf/2201.08845v1 Point-NeRF: Point-based Neural Radiance Fields http://arxiv.org/pdf/2201.08538v1 Computation of Regions of Attraction for Hybrid Limit Cycles Using Reachability: An Application to Walking Robots ./Link/2022-01-20 http://arxiv.org/pdf/2201.08363v1 Physics-informed neural networks for modeling rate- and temperature-dependent plasticity http://arxiv.org/pdf/2201.08348v1 Prediction of the electron density of states for crystalline compounds with Atomistic Line Graph Neural Networks (ALIGNN) http://arxiv.org/pdf/2201.08326v1 Learning with latent group sparsity via heat flow dynamics on networks http://arxiv.org/pdf/2201.08281v1 Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning http://arxiv.org/pdf/2201.08110v1 NNP/MM: Fast molecular dynamics simulations with machine learning potentials and molecular mechanics http://arxiv.org/pdf/2201.08082v1 Kernel Methods and Multi-layer Perceptrons Learn Linear Models in High Dimensions http://arxiv.org/pdf/2201.08025v1 Low-Pass Filtering SGD for Recovering Flat Optima in the Deep Learning Optimization Landscape http://arxiv.org/pdf/2201.07986v1 Unsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation http://arxiv.org/pdf/2201.08191v1 Three kinds of novel multi-symplectic methods for stochastic Hamiltonian partial differential equations ./Link/2022-01-19 http://arxiv.org/pdf/2201.07766v1 Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons http://arxiv.org/pdf/2201.07753v1 Deep Capsule Encoder-Decoder Network for Surrogate Modeling and Uncertainty Quantification http://arxiv.org/pdf/2201.07402v1 Flexible Parallel Learning in Edge Scenarios: Communication, Computational and Energy Cost http://arxiv.org/pdf/2201.07395v1 Overview frequency principle/spectral bias in deep learning http://arxiv.org/pdf/2201.07562v1 Learned Cone-Beam CT Reconstruction Using Neural Ordinary Differential Equations http://arxiv.org/pdf/2201.07483v1 Solutions of Nonlinear Optimal Control Problems Using Quasilinearization and Fenchel Duality http://arxiv.org/pdf/2201.07447v1 A unified algorithm for interfacial flows with incompressible and compressible fluids ./Link/2022-01-18 http://arxiv.org/pdf/2201.07082v1 Inducing Structure in Reward Learning by Learning Features http://arxiv.org/pdf/2201.06880v1 Temperature Field Inversion of Heat-Source Systems via Physics-Informed Neural Networks http://arxiv.org/pdf/2201.06848v1 High-Level Synthesis Performance Prediction using GNNs: Benchmarking, Modeling, and Advancing http://arxiv.org/pdf/2201.06821v1 Nonparametric Feature Selection by Random Forests and Deep Neural Networks http://arxiv.org/pdf/2201.06769v1 DEFER: Distributed Edge Inference for Deep Neural Networks http://arxiv.org/pdf/2201.06717v1 GTrans: Spatiotemporal Autoregressive Transformer with Graph Embeddings for Nowcasting Extreme Events http://arxiv.org/pdf/2201.06610v1 A Brief Survey of Machine Learning Methods for Emotion Prediction using Physiological Data http://arxiv.org/pdf/2201.06598v1 Fairness in Federated Learning for Spatial-Temporal Applications http://arxiv.org/pdf/2201.06463v1 Bayesian Calibration of imperfect computer models using Physics-informed priors http://arxiv.org/pdf/2201.06210v1 Deep convolutional neural network for shape optimization using level-set approach http://arxiv.org/pdf/2201.06126v1 Solving Inventory Management Problems with Inventory-dynamics-informed Neural Networks http://arxiv.org/pdf/2201.05978v1 Discrete Simulation Optimization for Tuning Machine Learning Method Hyperparameters http://arxiv.org/pdf/2201.05938v1 GradTail: Learning Long-Tailed Data Using Gradient-based Sample Weighting http://arxiv.org/pdf/2201.05868v1 Large-Scale Inventory Optimization: A Recurrent-Neural-Networks-Inspired Simulation Approach http://arxiv.org/pdf/2201.05867v1 Transferability in Deep Learning: A Survey http://arxiv.org/pdf/2201.05770v1 Edge-based Tensor prediction via graph neural networks http://arxiv.org/pdf/2201.06972v1 Representation Learning on Heterostructures via Heterogeneous Anonymous Walks http://arxiv.org/pdf/2201.06262v1 Optimisation of Structured Neural Controller Based on Continuous-Time Policy Gradient http://arxiv.org/pdf/2201.05835v1 Quantum estimation, control and learning: opportunities and challenges http://arxiv.org/pdf/2201.06676v1 Observing how deep neural networks understand physics through the energy spectrum of one-dimensional quantum mechanics ./Link/2022-01-14 http://arxiv.org/pdf/2201.05596v1 DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale http://arxiv.org/pdf/2201.05319v1 A Kernel-Expanded Stochastic Neural Network http://arxiv.org/pdf/2201.05599v1 Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement Learning http://arxiv.org/pdf/2201.05489v1 Emergence of Machine Language: Towards Symbolic Intelligence with Neural Networks http://arxiv.org/pdf/2201.05555v1 Adaptive symplectic model order reduction of parametric particle-based Vlasov-Poisson equatio http://arxiv.org/pdf/2201.05413v1 Evaluating Accuracy and Efficiency of HPC Solvers for Sparse Linear Systems with Applications to PDEs http://arxiv.org/pdf/2201.05392v1 A 1D-0D-3D coupled model for simulating blood flow and transport processes in breast tissue http://arxiv.org/pdf/2201.05487v1 Applications of Grassmannian and graph flows to coagulation systems http://arxiv.org/pdf/2201.05266v1 Model predictive control for robust quantum state preparation ./Link/2022-01-13 http://arxiv.org/pdf/2201.05149v1 The curse of overparametrization in adversarial training: Precise analysis of robust generalization for random features regression http://arxiv.org/pdf/2201.05136v1 Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders http://arxiv.org/pdf/2201.05098v1 Neural Koopman Lyapunov Control http://arxiv.org/pdf/2201.04976v1 Data-Driven Modeling and Prediction of Non-Linearizable Dynamics via Spectral Submanifolds http://arxiv.org/pdf/2201.04929v1 Improving VAE based molecular representations for compound property prediction http://arxiv.org/pdf/2201.04895v1 Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement Learning ./Link/2022-01-12 http://arxiv.org/pdf/2201.04609v1 GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules http://arxiv.org/pdf/2201.04545v1 On generalization bounds for deep networks based on loss surface implicit regularization http://arxiv.org/pdf/2201.04550v1 Data-driven feedback linearisation using model predictive control http://arxiv.org/pdf/2201.04339v1 Physics-guided Learning-based Adaptive Control on the SE(3) Manifold ./Link/2022-01-11 http://arxiv.org/pdf/2201.04093v1 Systematic Literature Review: Quantum Machine Learning and its applications http://arxiv.org/pdf/2201.04056v1 State Estimation in Electric Power Systems Leveraging Graph Neural Networks http://arxiv.org/pdf/2201.03822v1 Atomistic Simulations for Reactions and Spectroscopy in the Era of Machine Learning -- Quo Vadis? http://arxiv.org/pdf/2201.03733v1 The perfectly matched layer (PML) for hyperbolic wave propagation problems: A review ./Link/2022-01-10 http://arxiv.org/pdf/2201.03116v1 Opportunities of Hybrid Model-based Reinforcement Learning for Cell Therapy Manufacturing Process Development and Control http://arxiv.org/pdf/2201.03027v1 Meta-Generalization for Multiparty Privacy Learning to Identify Anomaly Multimedia Traffic in Graynet http://arxiv.org/pdf/2201.02978v1 Auto-Encoder based Co-Training Multi-View Representation Learning http://arxiv.org/pdf/2201.02874v1 Assessing Policy, Loss and Planning Combinations in Reinforcement Learning using a New Modular Architecture http://arxiv.org/pdf/2201.02867v1 Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscop http://arxiv.org/pdf/2201.02791v1 Scaling Knowledge Graph Embedding Models http://arxiv.org/pdf/2201.03308v1 Physics-Guided Neural Networks for Feedforward Control: An Orthogonal Projection-Based Approach http://arxiv.org/pdf/2201.03136v1 Data-driven Output-feedback Predictive Control: Unknown Plant's Order and Measurement Noise http://arxiv.org/pdf/2201.03262v1 Artificial Neural Networks Modelling of Wall Pressure Spectra Beneath Turbulent Boundary Layers http://arxiv.org/pdf/2201.02928v1 Frame invariant neural network closures for Kraichnan turbulence ./Link/2022-01-07 http://arxiv.org/pdf/2201.02596v1 Explainable deep learning for insights in El Nino and river flows http://arxiv.org/pdf/2201.02478v1 Bayesian Neural Networks for Reversible Steganography http://arxiv.org/pdf/2201.02469v1 Similarities and Differences between Machine Learning and Traditional Advanced Statistical Modeling in Healthcare Analytics http://arxiv.org/pdf/2201.02485v1 Automated Dissipation Control for Turbulence Simulation with Shell Models http://arxiv.org/pdf/2201.02356v1 Cross-Modality Deep Feature Learning for Brain Tumor Segmentation ./Link/2022-01-06 http://arxiv.org/pdf/2201.02177v1 Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets http://arxiv.org/pdf/2201.02067v1 Uncertainty Quantification Techniques for Space Weather Modeling: Thermospheric Density Application http://arxiv.org/pdf/2201.02028v1 A Light in the Dark: Deep Learning Practices for Industrial Computer Vision http://arxiv.org/pdf/2201.01943v1 Machine Learning: Algorithms, Models, and Applications http://arxiv.org/pdf/2201.01918v1 SABLAS: Learning Safe Control for Black-box Dynamical Systems http://arxiv.org/pdf/2201.01853v1 Mixture of basis for interpretable continual learning with distribution shifts http://arxiv.org/pdf/2201.01820v1 A Hybrid Quantum-Classical Neural Network Architecture for Binary Classification http://arxiv.org/pdf/2201.01289v1 Self-directed Machine Learning http://arxiv.org/pdf/2201.01288v1 Automated Graph Machine Learning: Approaches, Libraries and Directions http://arxiv.org/pdf/2201.01203v1 Quantifying Uncertainty in Deep Learning Approaches to Radio Galaxy Classification http://arxiv.org/pdf/2201.01032v1 Learning Operators with Coupled Attention http://arxiv.org/pdf/2201.00960v1 Neural Piecewise-Constant Delay Differential Equations http://arxiv.org/pdf/2201.00904v1 Deep neural networks for smooth approximation of physics with higher order and continuity B-spline base functions http://arxiv.org/pdf/2201.00801v1 Revisiting PGD Attacks for Stability Analysis of Large-Scale Nonlinear Systems and Perception-Based Control http://arxiv.org/pdf/2201.00726v1 Application of Machine Learning Methods in Inferring Surface Water Groundwater Exchanges using High Temporal Resolution Temperature Measurements http://arxiv.org/pdf/2201.00723v1 A Mixed Integer Programming Approach to Training Dense Neural Networks http://arxiv.org/pdf/2201.00565v1 Swift and Sure: Hardness-aware Contrastive Learning for Low-dimensional Knowledge Graph Embeddings http://arxiv.org/pdf/2201.01195v1 Super-resolution in Molecular Dynamics Trajectory Reconstruction with Bi-Directional Neural Networks http://arxiv.org/pdf/2201.00292v1 Fair Data Representation for Machine Learning at the Pareto Frontier http://arxiv.org/pdf/2201.00236v1 Operator Deep Q-Learning: Zero-Shot Reward Transferring in Reinforcement Learning http://arxiv.org/pdf/2201.00147v1 High-dimensional Bayesian Optimization Algorithm with Recurrent Neural Network for Disease Control Models in Time Series http://arxiv.org/pdf/2112.15421v1 Representation Learning via Consistent Assignment of Views to Clusters http://arxiv.org/pdf/2112.15348v1 Training Recurrent Neural Networks by Sequential Least Squares and the Alternating Direction Method of Multipliers http://arxiv.org/pdf/2201.00698v1 Deep-learning-based upscaling method for geologic models via theory-guided convolutional neural network http://arxiv.org/pdf/2201.01155v1 DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training http://arxiv.org/pdf/2112.15317v1 SplitBrain: Hybrid Data and Model Parallel Deep Learning http://arxiv.org/pdf/2112.15275v2 Learned Coarse Models for Efficient Turbulence Simulation http://arxiv.org/pdf/2112.15121v2 On the Role of Neural Collapse in Transfer Learning http://arxiv.org/pdf/2112.15094v1 Bayesian Algorithms Learn to Stabilize Unknown Continuous-Time Systems http://arxiv.org/pdf/2112.15072v1 Deep Learning Models for Knowledge Tracing: Review and Empirical Evaluation http://arxiv.org/pdf/2112.15034v1 Self Reward Design with Fine-grained Interpretability http://arxiv.org/pdf/2112.15019v1 Deep Transfer-Learning for patient specific model re-calibration: Application to sEMG-Classification http://arxiv.org/pdf/2201.00006v1 Knowledge intensive state design for traffic signal control http://arxiv.org/pdf/2112.14911v1 A Survey of Deep Learning Techniques for Dynamic Branch Prediction http://arxiv.org/pdf/2112.14826v1 PINNs for the Solution of the Hyperbolic Buckley-Leverett Problem with a Non-convex Flux Function http://arxiv.org/pdf/2112.14811v1 Active Learning-Based Optimization of Scientific Experimental Design http://arxiv.org/pdf/2112.14792v1 Graph Neural Networks for Communication Networks: Context, Use Cases and Opportunities http://arxiv.org/pdf/2112.14553v1 Active Learning of Quantum System Hamiltonians yields Query Advantage http://arxiv.org/pdf/2112.14551v1 Altitude Optimization of UAV Base Stations from Satellite Images Using Deep Neural Network http://arxiv.org/pdf/2112.14474v1 Bayesian Neural Hawkes Process for Event Uncertainty Prediction http://arxiv.org/pdf/2112.14448v1 A transfer learning enhanced the physics-informed neural network model for vortex-induced vibration http://arxiv.org/pdf/2112.14307v1 Ensemble Recognition in Reproducing Kernel Hilbert Spaces through Aggregated Measurements http://arxiv.org/pdf/2112.15445v1 Speedup deep learning models on GPU by taking advantage of efficient unstructured pruning and bit-width reduction http://arxiv.org/pdf/2112.14146v1 Towards continual task learning in artificial neural networks: current approaches and insights from neuroscience http://arxiv.org/pdf/2112.14040v1 Deep neural networks for solving forward and inverse problems of (2+1)-dimensional nonlinear wave equations with rational solitons http://arxiv.org/pdf/2201.00627v1 Uncertainty Detection in EEG Neural Decoding Models http://arxiv.org/pdf/2112.14012v1 Solving time dependent Fokker-Planck equations via temporal normalizing flow http://arxiv.org/pdf/2112.14769v1 Frame invariance and scalability of neural operators for partial differential equations 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Control System http://arxiv.org/pdf/2112.13284v1 Learning Linear Complementarity Systems http://arxiv.org/pdf/2112.14707v1 Physics-Informed Deep Operator Control: Controlling chaos in van der Pol oscillating circuits http://arxiv.org/pdf/2112.12999v1 Total Energy Shaping with Neural Interconnection and Damping Assignment -- Passivity Based Control http://arxiv.org/pdf/2112.12979v1 Integrating Physics-Based Modeling with Machine Learning for Lithium-Ion Batteries http://arxiv.org/pdf/2201.01617v1 Nonlinear lumped-parameter models for blood flow simulations in networks of vessels http://arxiv.org/pdf/2201.00498v1 Variational Inverting Network for Statistical Inverse Problems of Partial Differential Equations http://arxiv.org/pdf/2201.00146v1 Discovery of subdiffusion problem with noisy data via deep learning http://arxiv.org/pdf/2112.14523v1 Deep neural network approximation theory for high-dimensional functions http://arxiv.org/pdf/2112.14418v1 Deep adaptive basis Galerkin method for high-dimensional evolution equations with oscillatory solutions http://arxiv.org/pdf/2112.14038v1 DAS: A deep adaptive sampling method for solving partial differential equations http://arxiv.org/pdf/2112.14014v1 An Error Analysis Framework for Neural Network Modeling of Dynamical Systems http://arxiv.org/pdf/2112.13840v1 Shock trace prediction by reduced models for a viscous stochastic Burgers equation http://arxiv.org/pdf/2112.12892v1 Computing Viscous Flow Along a 3D Open Tube Using the Immerse Interface Method http://arxiv.org/pdf/2201.00722v1 Predicting Peak Stresses In Microstructured Materials Using Convolutional Encoder-Decoder Learning http://arxiv.org/pdf/2112.13541v1 Contracting dynamical systems in Banach spaces http://arxiv.org/pdf/2201.01318v1 Deep BSDE-ML Learning and Its Application to Model-Free Optimal Control http://arxiv.org/pdf/2201.00505v1 Superquantile-based learning: a direct approach using gradient-based optimization http://arxiv.org/pdf/2112.15392v1 High Dimensional Optimization through the Lens of Machine Learning http://arxiv.org/pdf/2201.01710v1 Deep Structured Neural Networks for Turbulence Closure Modelling http://arxiv.org/pdf/2201.01581v1 Bayesian comparison of stochastic models of dispersion http://arxiv.org/pdf/2201.01287v1 VCNN-e: A vector-cloud neural network with equivariance for emulating Reynolds stress transport equations http://arxiv.org/pdf/2201.00732v1 Inferring Turbulent Parameters via Machine Learning http://arxiv.org/pdf/2201.00222v1 Multi-fidelity Bayesian experimental design to quantify extreme-event statistics http://arxiv.org/pdf/2112.14172v1 Conditional moment methods for polydisperse cavitating flows http://arxiv.org/pdf/2112.13792v1 Investigating Steady Unconfined Groundwater Flow using Physics Informed Neural Networks http://arxiv.org/pdf/2201.01511v1 The long road to calibrated prediction uncertainty in computational chemistry http://arxiv.org/pdf/2112.15314v1 Quantum computational study of chloride ion attack on chloromethane for chemical accuracy and quantum noise effects with UCCSD and k-UpCCGSD ansatzes http://arxiv.org/pdf/2112.14077v1 Spatial, spin, and charge symmetry projections for a Fermi-Hubbard model on a quantum computer http://arxiv.org/pdf/2201.02172v1 Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation http://arxiv.org/pdf/2112.15577v2 Infinite width (finite depth) neural networks benefit from multi-task learning unlike shallow Gaussian Processes -- an exact quantitative macroscopic characterization http://arxiv.org/pdf/2112.13530v1 Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic ./Link/2021-12-23 http://arxiv.org/pdf/2112.12728v1 Latent Time Neural Ordinary Differential Equations http://arxiv.org/pdf/2112.12707v1 Improving Robustness and Uncertainty Modelling in Neural Ordinary Differential Equations http://arxiv.org/pdf/2112.12630v1 A Survey of Near-Data Processing Architectures for Neural Networks http://arxiv.org/pdf/2112.12509v1 Integrating Quantum Processor Device and Control Optimization in a Gradient-based Framework http://arxiv.org/pdf/2112.12506v1 Attentive Multi-View Deep Subspace Clustering Net http://arxiv.org/pdf/2112.12493v1 Equivariance and generalization in neural networks http://arxiv.org/pdf/2112.12474v1 Generalization capabilities of neural networks in lattice applications http://arxiv.org/pdf/2112.12321v1 Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks http://arxiv.org/pdf/2112.12344v1 Learning multiple regularization parameters for generalized Tikhonov regularization using multiple data sets without true data http://arxiv.org/pdf/2112.12419v1 Preparing ground states of the XXZ model using the quantum annealing with inductively coupled superconducting flux qubits http://arxiv.org/pdf/2112.12416v1 Partial Boolean functions computed by exact quantum 1-query algorithms ./Link/2021-12-22 http://arxiv.org/pdf/2112.12054v1 Machine Learning for Computational Science and Engineering -- a brief introduction and some critical questions http://arxiv.org/pdf/2112.11873v1 FLoBC: A Decentralized Blockchain-Based Federated Learning Framework http://arxiv.org/pdf/2112.11805v1 Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding http://arxiv.org/pdf/2112.11592v1 Neural Echo State Network using oscillations of gas bubbles in water: Computational validation by Mackey-Glass time series forecasting http://arxiv.org/pdf/2112.11640v1 Self-Distillation Mixup Training for Non-autoregressive Neural Machine Translation http://arxiv.org/pdf/2112.11667v1 Learning Based Model Predictive Control for Quadcopters with Dual Gaussian Process http://arxiv.org/pdf/2112.11950v1 POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier-Stokes equations http://arxiv.org/pdf/2112.11924v1 On 1-D PDE-Based Cardiovascular Flow Bottleneck Modeling and Analysis: A Vehicular Traffic Flow-Inspired Approach http://arxiv.org/pdf/2112.11534v1 Noise-injected analog Ising machines enable ultrafast statistical sampling and machine learning http://arxiv.org/pdf/2112.12144v1 Evolution of hybrid quantum-classical wavefunctions ./Link/2021-12-21 http://arxiv.org/pdf/2112.11397v1 NN2Poly: A polynomial representation for deep feed-forward artificial neural networks http://arxiv.org/pdf/2112.11360v1 Neural network guided adjoint computations in dual weighted residual error estimation http://arxiv.org/pdf/2112.11317v1 Deep Learning Based Cloud Cover Parameterization for ICON http://arxiv.org/pdf/2112.11279v1 A Pilot Study on Detecting Unfairness in Human Decisions With Machine Learning Algorithmic Bias Detection http://arxiv.org/pdf/2112.11239v1 Preserving gauge invariance in neural networks http://arxiv.org/pdf/2112.11210v1 Discrete fully probabilistic design: a tool to design control policies from examples http://arxiv.org/pdf/2112.11174v1 AutoCTS: Automated Correlated Time Series Forecasting -- Extended Version http://arxiv.org/pdf/2112.11172v1 Dynamically Stable Poincaré Embeddings for Neural Manifolds http://arxiv.org/pdf/2112.11331v1 Regularization from examples via neural networks for parametric inverse problems: topology matters ./Link/2021-12-20 http://arxiv.org/pdf/2112.10526v1 NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems http://arxiv.org/pdf/2112.10377v1 Learning for Robust Combinatorial Optimization: Algorithm and Application http://arxiv.org/pdf/2112.10254v1 Inverse deep learning methods and benchmarks for artificial electromagnetic material design http://arxiv.org/pdf/2112.10251v1 SSDNet: State Space Decomposition Neural Network for Time Series Forecasting http://arxiv.org/pdf/2112.10214v1 Modelling of Received Signals in Molecular Communication Systems based machine learning: Comparison of azure machine learning and Python tools http://arxiv.org/pdf/2112.10108v1 Investigation of Densely Connected Convolutional Networks with Domain Adversarial Learning for Noise Robust Speech Recognition http://arxiv.org/pdf/2112.10006v1 Low-resource Learning with Knowledge Graphs: A Comprehensive Survey http://arxiv.org/pdf/2112.09968v1 Being Friends Instead of Adversaries: Deep Networks Learn from Data Simplified by Other Networks http://arxiv.org/pdf/2112.10390v1 Evaluation and Comparison of Deep Learning Methods for Pavement Crack Identification with Visual Images http://arxiv.org/pdf/2112.10320v1 Robust Data-Driven Linear Power Flow Model with Probability Constrained Worst-Case Errors http://arxiv.org/pdf/2112.09963v1 The Kolmogorov Superposition Theorem can Break the Curse of Dimensionality When Approximating High Dimensional Functions ./Link/2021-12-17 http://arxiv.org/pdf/2112.09684v1 On the existence of global minima and convergence analyses for gradient descent methods in the training of deep neural networks http://arxiv.org/pdf/2112.09668v1 Deep Learning for Spatiotemporal Modeling of Urbanization http://arxiv.org/pdf/2112.09641v1 Embedding Graph Convolutional Networks in Recurrent Neural Networks for Predictive Monitoring http://arxiv.org/pdf/2112.09483v1 Learning from Heterogeneous Data Based on Social Interactions over Graphs http://arxiv.org/pdf/2112.09468v1 Towards fuzzification of adaptation rules in self-adaptive architectures http://arxiv.org/pdf/2112.09391v1 Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges http://arxiv.org/pdf/2112.09681v1 Calibrating hypersonic turbulence flow models with the HIFiRE-1 experiment using data-driven machine-learned models http://arxiv.org/pdf/2112.09302v1 Three-dimensional deep learning-based reduced order model for unsteady flow dynamics with variable Reynolds number ./Link/2021-12-16 http://arxiv.org/pdf/2112.09025v1 Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs http://arxiv.org/pdf/2112.08676v1 Machine Learning-Accelerated Computational Solid Mechanics: Application to Linear Elasticity http://arxiv.org/pdf/2112.08645v1 Learning Interpretable Models Through Multi-Objective Neural Architecture Search http://arxiv.org/pdf/2112.08823v1 Error-Tolerant Geometric Quantum Control for Logical Qubits with Minimal Resource ./Link/2021-12-15 http://arxiv.org/pdf/2112.08297v1 Rethinking Influence Functions of Neural Networks in the Over-parameterized Regime http://arxiv.org/pdf/2112.08268v1 Prescriptive Machine Learning for Automated Decision Making: Challenges and Opportunities http://arxiv.org/pdf/2112.08091v1 Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems http://arxiv.org/pdf/2112.07995v1 Domain-informed neural networks for interaction localization within astroparticle experiments http://arxiv.org/pdf/2112.07893v1 Graph-based Ensemble Machine Learning for Student Performance Prediction http://arxiv.org/pdf/2112.07805v1 Network Graph Based Neural Architecture Search http://arxiv.org/pdf/2112.08148v1 Composed Physics- and Data-driven System Identification for Non-autonomous Systems in Control Engineering http://arxiv.org/pdf/2112.07983v1 Data-Driven Models for Control Engineering Applications Using the Koopman Operator ./Link/2021-12-14 http://arxiv.org/pdf/2112.07535v1 Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning http://arxiv.org/pdf/2112.07485v1 Pruning Coherent Integrated Photonic Neural Networks Using the Lottery Ticket Hypothesis http://arxiv.org/pdf/2112.07184v1 Calibrated and Sharp Uncertainties in Deep Learning via Simple Density Estimation http://arxiv.org/pdf/2112.07096v1 Adaptive Projected Residual Networks for Learning Parametric Maps from Sparse Data http://arxiv.org/pdf/2112.07403v1 Stochastic Actor-Executor-Critic for Image-to-Image Translation http://arxiv.org/pdf/2112.07331v1 Non-Iterative Calculation of Quasi-Dynamic Energy Flow in the Heat and Electricity Integrated Energy Systems http://arxiv.org/pdf/2112.07238v1 Composing MPC with LQR and Neural Networks for Efficient and Stable Control ./Link/2021-12-13 http://arxiv.org/pdf/2112.06685v1 Quaternion-Valued Convolutional Neural Network Applied for Acute Lymphoblastic Leukemia Diagnosis http://arxiv.org/pdf/2112.06409v1 Data Collection and Quality Challenges in Deep Learning: A Data-Centric AI Perspective http://arxiv.org/pdf/2112.06351v1 Neural Point Process for Learning Spatiotemporal Event Dynamics http://arxiv.org/pdf/2112.06281v1 Spatial-Temporal-Fusion BNN: Variational Bayesian Feature Layer http://arxiv.org/pdf/2112.06206v1 Automatic differentiation approach for reconstructing spectral functions with neural networks http://arxiv.org/pdf/2112.06142v1 Semi-supervised teacher-student deep neural network for materials discovery http://arxiv.org/pdf/2112.06044v1 Achieving Low Complexity Neural Decoders via Iterative Pruning http://arxiv.org/pdf/2112.05934v1 SPDCinv: Inverse Quantum-Optical Design of High-Dimensional Qudits http://arxiv.org/pdf/2112.06419v1 Stacked Generative Machine Learning Models for Fast Approximations of Steady-State Navier-Stokes Equations http://arxiv.org/pdf/2112.06759v1 Hformer: Hybrid CNN-Transformer for Fringe Order Prediction in Phase Unwrapping of Fringe Projection ./Link/2021-12-10 http://arxiv.org/pdf/2112.05687v1 Federated Two-stage Learning with Sign-based Voting http://arxiv.org/pdf/2112.05620v1 How to Avoid Trivial Solutions in Physics-Informed Neural Networks http://arxiv.org/pdf/2112.05609v1 Interaction-Aware Sensitivity Analysis for Aerodynamic Optimization Results using Information Theory http://arxiv.org/pdf/2112.05489v1 Surrogate-data-enriched Physics-Aware Neural Networks http://arxiv.org/pdf/2112.05451v1 Structure-Preserving Learning Using Gaussian Processes and Variational Integrators http://arxiv.org/pdf/2112.05313v1 Building Autocorrelation-Aware Representations for Fine-Scale Spatiotemporal Prediction http://arxiv.org/pdf/2112.05310v1 Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach http://arxiv.org/pdf/2112.05258v1 Direct 0D-3D coupling of a lattice Boltzmann methodology for fluid-structure hemodynamics simulations http://arxiv.org/pdf/2112.05257v1 A new pseudo-spectral methodology without numerical diffusion for conducting dye simulations and particle residence time calculations http://arxiv.org/pdf/2112.05735v1 Deterministic particle flows for constraining stochastic nonlinear systems ./Link/2021-12-09 http://arxiv.org/pdf/2112.05005v1 Mutual Adversarial Training: Learning together is better than going alone http://arxiv.org/pdf/2112.04979v1 A fully-differentiable compressible high-order computational fluid dynamics solver http://arxiv.org/pdf/2112.04963v1 Model-Agnostic Hybrid Numerical Weather Prediction and Machine Learning Paradigm for Solar Forecasting in the Tropics http://arxiv.org/pdf/2112.04643v1 Autoregressive Quantile Flows for Predictive Uncertainty Estimation http://arxiv.org/pdf/2112.04721v1 One-dimensional Deep Low-rank and Sparse Network for Accelerated MRI ./Link/2021-12-08 http://arxiv.org/pdf/2112.04439v1 Data-driven stochastic model predictive control http://arxiv.org/pdf/2112.04085v1 KoopmanizingFlows: Diffeomorphically Learning Stable Koopman Operators http://arxiv.org/pdf/2112.03805v1 Learning nonlinear feedforward: a Gaussian Process Approach Applied to a Printer with Friction http://arxiv.org/pdf/2112.03347v1 Structured learning of safety guarantees for the control of uncertain dynamical systems http://arxiv.org/pdf/2112.02561v1 Learning-Based Control Compensation for Multi-Axis Gimbal Systems Using Inverse and Forward Dynamics http://arxiv.org/pdf/2112.04307v1 Physics-informed dynamic mode decomposition (piDMD) http://arxiv.org/pdf/2112.03749v1 Interpolating between BSDEs and PINNs -- deep learning for elliptic and parabolic boundary value problems http://arxiv.org/pdf/2112.04062v1 Data-driven forward-inverse problems and modulational instability for Yajima-Oikawa system using deep learning with parameter regularization http://arxiv.org/pdf/2112.03915v1 GraDIRN: Learning Iterative Gradient Descent-based Energy Minimization for Deformable Image Registration http://arxiv.org/pdf/2112.04029v1 Boundary Control and Estimation for Under-Balanced Drilling with Uncertain Reservoir Parameters http://arxiv.org/pdf/2112.02751v1 Robust training approach of neural networks for fluid flow state estimations http://arxiv.org/pdf/2112.03585v1 Quantum readout error mitigation via deep learning http://arxiv.org/pdf/2112.02655v1 Quantum Machine Learning for Radio Astronomy http://arxiv.org/pdf/2112.04364v1 Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks http://arxiv.org/pdf/2112.03968v1 Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks http://arxiv.org/pdf/2112.03202v1 Collective variable discovery in the age of machine learning: reality, hype and everything in between http://arxiv.org/pdf/2112.04453v1 MLP Architectures for Vision-and-Language Modeling: An Empirical Study http://arxiv.org/pdf/2112.04364v1 Generalization Error Bounds for Iterative Recovery Algorithms Unfolded as Neural Networks http://arxiv.org/pdf/2112.04085v1 KoopmanizingFlows: Diffeomorphically Learning Stable Koopman Operators http://arxiv.org/pdf/2112.04148v1 Neural Points: Point Cloud Representation with Neural Fields ./Link/2021-12-07 http://arxiv.org/pdf/2112.03860v1 Traversing within the Gaussian Typical Set: Differentiable Gaussianization Layers for Inverse Problems Augmented by Normalizing Flows http://arxiv.org/pdf/2112.03815v2 Accurate parameter estimation using scan-specific unsupervised deep learning for relaxometry and MR fingerprinting http://arxiv.org/pdf/2112.03773v1 On the Effectiveness of Mode Exploration in Bayesian Model Averaging for Neural Networks http://arxiv.org/pdf/2112.03765v1 In-flight Novelty Detection with Convolutional Neural Networks http://arxiv.org/pdf/2112.03734v1 Towards Modeling and Resolving Singular Parameter Spaces using Stratifolds http://arxiv.org/pdf/2112.03732v1 A coarse space acceleration of deep-DDM http://arxiv.org/pdf/2112.03728v1 Flexible Networks for Learning Physical Dynamics of Deformable Objects http://arxiv.org/pdf/2112.03609v1 Predict and Optimize: Through the Lens of Learning to Rank http://arxiv.org/pdf/2112.03588v1 A deep language model to predict metabolic network equilibria http://arxiv.org/pdf/2112.03566v1 More layers! End-to-end regression and uncertainty on tabular data with deep learning http://arxiv.org/pdf/2112.03528v1 Physics guided deep learning generative models for crystal materials discovery http://arxiv.org/pdf/2112.03508v1 Training Deep Models to be Explained with Fewer Examples http://arxiv.org/pdf/2112.03478v1 Generative Adversarial Networks for Labeled Data Creation for Structural Damage Detection http://arxiv.org/pdf/2112.03469v1 Emulating Spatio-Temporal Realizations of Three-Dimensional Isotropic Turbulence via Deep Sequence Learning Models http://arxiv.org/pdf/2112.03383v1 Graph Neural Networks Accelerated Molecular Dynamics http://arxiv.org/pdf/2112.03364v1 Scalable Geometric Deep Learning on Molecular Graphs http://arxiv.org/pdf/2112.03321v1 Noether Networks: Meta-Learning Useful Conserved Quantities http://arxiv.org/pdf/2112.03235v1 Simulation Intelligence: Towards a New Generation of Scientific Methods http://arxiv.org/pdf/2112.03215v1 Multi-scale Feature Learning Dynamics: Insights for Double Descent http://arxiv.org/pdf/2112.03212v1 Physically Consistent Neural Networks for building thermal modeling: theory and analysis http://arxiv.org/pdf/2112.03041v1 Keeping it Simple: Language Models can learn Complex Molecular Distributions http://arxiv.org/pdf/2112.02663v1 ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting http://arxiv.org/pdf/2112.02625v1 Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View http://arxiv.org/pdf/2112.02424v1 Variational Wasserstein gradient flow http://arxiv.org/pdf/2112.03698v1 Modeling and Predicting Blood Flow Characteristics through Double Stenosed Artery from CFD simulation using Deep Learning Models http://arxiv.org/pdf/2112.03643v1 QKSA: Quantum Knowledge Seeking Agent -- resource-optimized reinforcement learning using quantum process tomography ./Link/2021-12-03 http://arxiv.org/pdf/2112.01878v1 Fast $L^2$ optimal mass transport via reduced basis methods for the Monge-Amp$\grave{\rm e}$re equation http://arxiv.org/pdf/2112.01830v1 Table2Vec: Automated Universal Representation Learning to Encode All-round Data DNA for Benchmarkable and Explainable Enterprise Data Science http://arxiv.org/pdf/2112.01741v1 Frame Averaging for Equivariant Shape Space Learning http://arxiv.org/pdf/2112.01687v1 Differential Property Prediction: A Machine Learning Approach to Experimental Design in Advanced Manufacturing http://arxiv.org/pdf/2112.01675v1 Challenges and Opportunities in Approximate Bayesian Deep Learning for Intelligent IoT Systems http://arxiv.org/pdf/2112.01653v1 Learning Curves for Sequential Training of Neural Networks: Self-Knowledge Transfer and Forgetting http://arxiv.org/pdf/2112.01652v1 Data-enabled Gradient Flow as Feedback Controller: Regulation of Linear Dynamical Systems to Minimizers of Unknown Functions http://arxiv.org/pdf/2112.01696v1 A hybrid physics-informed neural network for nonlinear partial differential equation http://arxiv.org/pdf/2112.01927v1 Solving hadron structures with variational quantum eigensolvers http://arxiv.org/pdf/2112.01655v1 Quantum Algorithm for Solving Quadratic Nonlinear System of Equations ./Link/2021-12-02 http://arxiv.org/pdf/2112.01477v1 Why Calibration Error is Wrong Given Model Uncertainty: Using Posterior Predictive Checks with Deep Learning http://arxiv.org/pdf/2112.01475v1 A Hybrid Science-Guided Machine Learning Approach for Modeling and Optimizing Chemical Processes http://arxiv.org/pdf/2112.01438v1 Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximation http://arxiv.org/pdf/2112.01433v1 Loss Landscape Dependent Self-Adjusting Learning Rates in Decentralized Stochastic Gradient Descent http://arxiv.org/pdf/2112.01423v1 Training Efficiency and Robustness in Deep Learning http://arxiv.org/pdf/2112.01388v1 Residual Pathway Priors for Soft Equivariance Constraints http://arxiv.org/pdf/2112.01334v1 Stationary Diffusion State Neural Estimation for Multiview Clustering http://arxiv.org/pdf/2112.01254v1 Hierarchical Learning to Solve Partial Differential Equations Using Physics-Informed Neural Networks http://arxiv.org/pdf/2112.01088v1 Constrained Machine Learning: The Bagel Framework http://arxiv.org/pdf/2112.00988v1 Deep Transfer Learning: A Novel Collaborative Learning Model for Cyberattack Detection Systems in IoT Networks http://arxiv.org/pdf/2112.00976v1 Gaussian Mixture Variational Autoencoder with Contrastive Learning for Multi-Label Classification http://arxiv.org/pdf/2112.00952v1 A Discrete-event-based Simulator for Deep Learning at Edge http://arxiv.org/pdf/2112.00913v1 CDLNet: Noise-Adaptive Convolutional Dictionary Learning Network for Blind Denoising and Demosaicing http://arxiv.org/pdf/2112.01113v1 Probabilistic neural networks for predicting energy dissipation rates in geophysical turbulent flows ./Link/2021-12-01 http://arxiv.org/pdf/2112.00723v1 Infinite Neural Network Quantum States http://arxiv.org/pdf/2112.00698v1 CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems http://arxiv.org/pdf/2112.00639v1 Robustness in Deep Learning for Computer Vision: Mind the gap? http://arxiv.org/pdf/2112.00544v1 Molecular Contrastive Learning with Chemical Element Knowledge Graph http://arxiv.org/pdf/2112.00534v1 Empirical evaluation of shallow and deep learning classifiers for Arabic sentiment analysis http://arxiv.org/pdf/2112.00324v1 Optimizing for In-memory Deep Learning with Emerging Memory Technology http://arxiv.org/pdf/2112.00275v1 Learning from Mistakes based on Class Weighting with Application to Neural Architecture Search http://arxiv.org/pdf/2112.00220v1 A generic physics-informed neural network-based framework for reliability assessment of multi-state systems http://arxiv.org/pdf/2112.00713v1 hIPPYlib-MUQ: A Bayesian Inference Software Framework for Integration of Data with Complex Predictive Models under Uncertainty http://arxiv.org/pdf/2112.00385v1 Deep-learning Assisted Extraction of Fluid Velocity from Scalar Signal Transport in a Shallow Microfluidic Channel http://arxiv.org/pdf/2112.00652v1 Graph neural networks for fast electron density estimation of molecules, liquids, and solids ./Link/2021-11-30 http://arxiv.org/pdf/2111.15605v1 Synthetic weather radar using hybrid quantum-classical machine learning http://arxiv.org/pdf/2111.15597v1 Surrogate-based optimization using an artificial neural network for a parameter identification in a 3D marine ecosystem model http://arxiv.org/pdf/2111.15146v1 Molecular Attributes Transfer from Non-Parallel Data http://arxiv.org/pdf/2111.15112v1 AugLiChem: Data Augmentation Library ofChemical Structures for Machine Learning http://arxiv.org/pdf/2111.15163v1 Stochastic Wasserstein Hamiltonian Flows http://arxiv.org/pdf/2111.15120v1 Sequential Stochastic Control (Single or Multi-Agent) Problems Nearly Admit Change of Measures with Independent Measurements ./Link/2021-11-29 http://arxiv.org/pdf/2111.14767v1 A Graph Deep Learning Framework for High-Level Synthesis Design Space Exploration http://arxiv.org/pdf/2111.14760v1 Reconstructing spectral functions via automatic differentiation http://arxiv.org/pdf/2111.14352v1 Physics-informed Evolutionary Strategy based Control for Mitigating Delayed Voltage Recovery http://arxiv.org/pdf/2111.14338v1 Improving Deep Learning Interpretability by Saliency Guided Training http://arxiv.org/pdf/2111.14220v1 On the Robustness and Generalization of Deep Learning Driven Full Waveform Inversion http://arxiv.org/pdf/2111.14151v1 Learning Physical Concepts in Cyber-Physical Systems: A Case Study http://arxiv.org/pdf/2111.14038v1 Learning Wildfire Model from Incomplete State Observations http://arxiv.org/pdf/2111.13964v1 Benchmarking Accuracy and Generalizability of Four Graph Neural Networks Using Large In Vitro ADME Datasets from Different Chemical Spaces http://arxiv.org/pdf/2111.13861v1 AIS: A nonlinear activation function for industrial safety engineering http://arxiv.org/pdf/2111.13812v1 Achieving an Accurate Random Process Model for PV Power using Cheap Data: Leveraging the SDE and Public Weather Reports http://arxiv.org/pdf/2111.13802v1 Factorized Fourier Neural Operators http://arxiv.org/pdf/2111.13786v1 Learning from learning machines: a new generation of AI technology to meet the needs of science http://arxiv.org/pdf/2111.14122v1 Cross-Task Consistency Learning Framework for Multi-Task Learning http://arxiv.org/pdf/2111.13926v1 An Ensemble Variational Fokker-Planck Method for Data Assimilation http://arxiv.org/pdf/2111.13906v1 A data-driven partitioned approach for the resolution of time-dependent optimal control problems with dynamic mode decomposition http://arxiv.org/pdf/2111.14499v1 Periodic and chaotic dynamics in a map-based neuron model http://arxiv.org/pdf/2111.14041v1 Learning Quantum Finite Automata with Queries ./Link/2021-11-26 http://arxiv.org/pdf/2111.13674v1 Neural Fields as Learnable Kernels for 3D Reconstruction http://arxiv.org/pdf/2111.13311v1 Blaschke Product Neural Networks (BPNN): A Physics-Infused Neural Network for Phase Retrieval of Meromorphic Functions http://arxiv.org/pdf/2111.13296v1 Approximate Bayesian Computation for Physical Inverse Modeling http://arxiv.org/pdf/2111.13236v1 Joint inference and input optimization in equilibrium networks http://arxiv.org/pdf/2111.13207v1 Characteristic Neural Ordinary Differential Equations http://arxiv.org/pdf/2111.13185v1 Learning Conditional Invariance through Cycle Consistency http://arxiv.org/pdf/2111.13171v1 Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks http://arxiv.org/pdf/2111.13154v1 Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning http://arxiv.org/pdf/2111.13139v1 Group equivariant neural posterior estimation http://arxiv.org/pdf/2111.13075v1 Predicting the success of Gradient Descent for a particular Dataset-Architecture-Initialization (DAI) http://arxiv.org/pdf/2111.13073v1 Neuronal Learning Analysis using Cycle-Consistent Adversarial Networks http://arxiv.org/pdf/2111.13037v1 Learning dynamical systems from data: A simple cross-validation perspective, part III: Irregularly-Sampled Time Series http://arxiv.org/pdf/2111.12995v1 Learning Low-Dimensional Quadratic-Embeddings of High-Fidelity Nonlinear Dynamics using Deep Learning http://arxiv.org/pdf/2111.12963v1 Error Bounds for a Matrix-Vector Product Approximation with Deep ReLU Neural Networks http://arxiv.org/pdf/2111.12861v1 A Deep Learning Approach for Macroscopic Energy Consumption Prediction with Microscopic Quality for Electric Vehicles http://arxiv.org/pdf/2111.12906v1 Robustness against Adversarial Attacks in Neural Networks using Incremental Dissipativity http://arxiv.org/pdf/2111.13246v1 Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference http://arxiv.org/pdf/2111.13372v1 A deep learning based reduced order modeling for stochastic underground flow problems http://arxiv.org/pdf/2111.13469v1 High Reynolds number airfoil turbulence modeling method based on machine learning technique ./Link/2021-11-24 http://arxiv.org/pdf/2111.12606v1 Deep metric learning improves lab of origin prediction of genetically engineered plasmids http://arxiv.org/pdf/2111.12600v1 Learning State Representations via Retracing in Reinforcement Learning http://arxiv.org/pdf/2111.12594v1 Conditional Object-Centric Learning from Video http://arxiv.org/pdf/2111.12545v1 Learning to Refit for Convex Learning Problems http://arxiv.org/pdf/2111.12506v1 A Unified Approach to Variational Autoencoders and Stochastic Normalizing Flows via Markov Chains http://arxiv.org/pdf/2111.12541v1 Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model http://arxiv.org/pdf/2111.12511v1 Deep learning-based reduced order models for the real-time simulation of the nonlinear dynamics of microstructures http://arxiv.org/pdf/2111.12451v1 Geometrically reduced modelling of pulsatile flow in perivascular networks http://arxiv.org/pdf/2111.12408v1 Markov Chain Generative Adversarial Neural Networks for Solving Bayesian Inverse Problems in Physics Applications http://arxiv.org/pdf/2111.12437v1 Efficient quantum computation of molecular forces and other energy gradients ./Link/2021-11-23 http://arxiv.org/pdf/2111.12066v1 Physics Informed Neural Networks for Control Oriented Thermal Modeling of Buildings http://arxiv.org/pdf/2111.12050v1 Simple Stochastic and Online Gradient DescentAlgorithms for Pairwise Learning http://arxiv.org/pdf/2111.11798v1 Composing Partial Differential Equations with Physics-Aware Neural Networks http://arxiv.org/pdf/2111.11702v1 Deep learning-based fast solver of the shallow water equations http://arxiv.org/pdf/2111.11654v1 Weight Pruning and Uncertainty in Radio Galaxy Classification http://arxiv.org/pdf/2111.11660v1 Non-invasive hemodynamic analysis for aortic regurgitation using computational fluid dynamics and deep learning http://arxiv.org/pdf/2111.11617v1 State Estimation of the Stefan PDE: A Tutorial on Design and Applications to Polar Ice and Batteries http://arxiv.org/pdf/2111.11648v1 Phase-Field Modeling and Peridynamics for Defect Dynamics, and an Augmented Phase-Field Model with Viscous Stresses ./Link/2021-11-22 http://arxiv.org/pdf/2111.11426v1 Neural Fields in Visual Computing and Beyond http://arxiv.org/pdf/2111.11344v1 Modeling Irregular Time Series with Continuous Recurrent Units http://arxiv.org/pdf/2111.11168v1 Towards Understanding the Unreasonable Effectiveness of Learning AC-OPF Solutions http://arxiv.org/pdf/2111.10898v1 Renewable energy integration and microgrid energy trading using multi-agent deep reinforcement learning http://arxiv.org/pdf/2111.10857v1 Accretionary Learning with Deep Neural Networks http://arxiv.org/pdf/2111.10847v1 Calibrated Diffusion Tensor Estimation http://arxiv.org/pdf/2111.10752v1 Stochastic Variance Reduced Ensemble Adversarial Attack for Boosting the Adversarial Transferability http://arxiv.org/pdf/2111.10695v1 Image-Like Graph Representations for Improved Molecular Property Prediction http://arxiv.org/pdf/2111.10489v1 Modeling Design and Control Problems Involving Neural Network Surrogates http://arxiv.org/pdf/2111.10992v1 Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and control http://arxiv.org/pdf/2111.10947v1 Comparison of Numerical Solvers for Differential Equations for Holonomic Gradient Method in Statistics http://arxiv.org/pdf/2111.10693v1 Thermodynamical Material Networks for Modeling, Planning and Control of Circular Material Flows http://arxiv.org/pdf/2111.11299v1 Data-driven unsteady aeroelastic modeling for control http://arxiv.org/pdf/2111.11185v1 Hybrid Neural Network PDE Solvers for Reacting Flows http://arxiv.org/pdf/2111.10654v1 A method for preserving nominally-resolved flow patterns in low-resolution ocean simulations: Constrained dynamics http://arxiv.org/pdf/2111.10551v1 Closures for Multi-Component Reacting Flows based on Dispersion Analysis http://arxiv.org/pdf/2111.10956v1 Quantum reservoir computing using arrays of Rydberg atoms ./Link/2021-11-19 http://arxiv.org/pdf/2111.10329v1 Physics-enhanced Neural Networks in the Small Data Regime http://arxiv.org/pdf/2111.10285v1 Adversarial Deep Learning for Online Resource Allocation http://arxiv.org/pdf/2111.10192v1 An Expectation-Maximization Perspective on Federated Learning http://arxiv.org/pdf/2111.09993v1 Esophageal virtual disease landscape using mechanics-informed machine learning ./Link/2021-11-18 http://arxiv.org/pdf/2111.09679v1 Enhanced Membership Inference Attacks against Machine Learning Models http://arxiv.org/pdf/2111.09489v1 Data-driven discovery of Bäcklund transforms and soliton evolution equations via deep neural network learning schemes http://arxiv.org/pdf/2111.09562v1 COMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression http://arxiv.org/pdf/2111.09639v1 Recurrent Variational Network: A Deep Learning Inverse Problem Solver applied to the task of Accelerated MRI Reconstruction http://arxiv.org/pdf/2111.09727v1 The Strong Integral Input-to-State Stability Property in Dynamical Flow Networks http://arxiv.org/pdf/2111.09504v1 A comparative study on how neural networks enhance quantum state tomography http://arxiv.org/pdf/2111.09880v1 Optimal control of PDEs using physics-informed neural networks ./Link/2021-11-17 http://arxiv.org/pdf/2111.09128v1 Smart Data Representations: Impact on the Accuracy of Deep Neural Networks http://arxiv.org/pdf/2111.08900v1 A GNN-RNN Approach for Harnessing Geospatial and Temporal Information: Application to Crop Yield Prediction http://arxiv.org/pdf/2111.08848v1 GNN-DSE: Automated Accelerator Optimization Aided by Graph Neural Networks http://arxiv.org/pdf/2111.08853v1 NNSynth: Neural Network Guided Abstraction-Based Controller Synthesis for Stochastic Systems http://arxiv.org/pdf/2111.09086v1 Deep learning based on mixed-variable physics informed neural network for solving fluid dynamics without simulation data ./Link/2021-11-16 http://arxiv.org/pdf/2111.08626v1 Adjoint-Matching Neural Network Surrogates for Fast 4D-Var Data Assimilation http://arxiv.org/pdf/2111.08577v1 Neuron-based Pruning of Deep Neural Networks with Better Generalization using Kronecker Factored Curvature Approximation http://arxiv.org/pdf/2111.08565v1 Polymatrix Competitive Gradient Descent http://arxiv.org/pdf/2111.08410v1 Thoughts on the Consistency between Ricci Flow and Neural Network Behavior http://arxiv.org/pdf/2111.08275v1 Deep Distilling: automated code generation using explainable deep learning http://arxiv.org/pdf/2111.08206v1 JMSNAS: Joint Model Split and Neural Architecture Search for Learning over Mobile Edge Networks http://arxiv.org/pdf/2111.08349v1 SEnSeI: A Deep Learning Module for Creating Sensor Independent Cloud Masks http://arxiv.org/pdf/2111.08256v1 Online Meta Adaptation for Variable-Rate Learned Image Compression http://arxiv.org/pdf/2111.08445v1 Conjugate gradient MIMO iterative learning control using data-driven stochastic gradients http://arxiv.org/pdf/2111.08389v1 Analysis of Model-Free Reinforcement Learning Control Schemes on self-balancing Wheeled Extendible System http://arxiv.org/pdf/2111.08339v1 Global Sensitivity Analysis of Four Chamber Heart Hemodynamics Using Surrogate Models http://arxiv.org/pdf/2111.08288v1 Quantum Heaviside Eigen Solver http://arxiv.org/pdf/2111.08696v1 Normalizing flows for atomic solids ./Link/2021-11-15 http://arxiv.org/pdf/2111.07911v1 On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networks http://arxiv.org/pdf/2111.07737v1 Progress in Self-Certified Neural Networks http://arxiv.org/pdf/2111.07683v1 Reachability analysis of neural networks using mixed monotonicity http://arxiv.org/pdf/2111.07671v1 NeuralPDE: Modelling Dynamical Systems from Data http://arxiv.org/pdf/2111.07470v1 Skillful Twelve Hour Precipitation Forecasts using Large Context Neural Networks http://arxiv.org/pdf/2111.07284v1 Energy Efficient Learning with Low Resolution Stochastic Domain Wall Synapse Based Deep Neural Networks http://arxiv.org/pdf/2111.07167v1 The Three Stages of Learning Dynamics in High-Dimensional Kernel Methods http://arxiv.org/pdf/2111.07058v1 Bolstering Stochastic Gradient Descent with Model Building http://arxiv.org/pdf/2111.07393v1 DEEP: DEnoising Entity Pre-training for Neural Machine Translation http://arxiv.org/pdf/2111.07263v1 Code Representation Learning with Prüfer Sequences http://arxiv.org/pdf/2111.07004v1 Fault Diagnosis of Nonlinear Systems Using a Hybrid-Degree Dual Cubature-based Estimation Scheme http://arxiv.org/pdf/2111.07500v1 Distributionally Robust Expected Residual Minimization for Stochastic Variational Inequality Problems http://arxiv.org/pdf/2111.07322v1 CSG: A stochastic gradient method for a wide class of optimization problems appearing in a machine learning or data-driven context http://arxiv.org/pdf/2111.07937v1 Data-driven prediction of complex flow field over an axisymmetric body of revolution using Machine Learning http://arxiv.org/pdf/2111.07043v1 Reynolds Stress Modeling Using Data Driven Machine Learning Algorithms http://arxiv.org/pdf/2111.07553v1 Provable Advantage in Quantum Phase Learning via Quantum Kernel Alphatron http://arxiv.org/pdf/2111.07486v1 Quantum homotopy perturbation method for nonlinear dissipative ordinary differential equations ./Link/2021-11-12 http://arxiv.org/pdf/2111.06862v1 Monolithic Silicon Photonic Architecture for Training Deep Neural Networks with Direct Feedback Alignment http://arxiv.org/pdf/2111.06841v1 A posteriori learning of quasi-geostrophic turbulence parametrization: an experiment on integration steps http://arxiv.org/pdf/2111.06783v1 Can neural networks predict dynamics they have never seen? http://arxiv.org/pdf/2111.06679v1 deepstruct -- linking deep learning and graph theory http://arxiv.org/pdf/2111.06642v1 Application of Neural Network Machine Learning to Solution of Black-Scholes Equation ./Link/2021-11-11 http://arxiv.org/pdf/2111.06393v1 Super-resolving Dark Matter Halos using Generative Deep Learning http://arxiv.org/pdf/2111.06376v1 Quantum Model-Discovery http://arxiv.org/pdf/2111.06211v1 Model-Based Reinforcement Learning for Stochastic Hybrid Systems http://arxiv.org/pdf/2111.06063v1 On the Equivalence between Neural Network and Support Vector Machine http://arxiv.org/pdf/2111.06011v1 Climate Modeling with Neural Diffusion Equations http://arxiv.org/pdf/2111.06084v1 On the Problem of Reformulating Systems with Uncertain Dynamics as a Stochastic Differential Equation ./Link/2021-11-10 http://arxiv.org/pdf/2111.05841v1 Physics-enhanced deep surrogates for PDEs http://arxiv.org/pdf/2111.05820v1 Multi-Task Neural Processes http://arxiv.org/pdf/2111.05641v1 Parallel Physics-Informed Neural Networks with Bidirectional Balance http://arxiv.org/pdf/2111.05646v1 Quantum Amplitude Damping for solving homogeneous linear differential equations: a non-interferometric algorithm ./Link/2021-11-09 http://arxiv.org/pdf/2111.05323v1 Variational Multi-Task Learning with Gumbel-Softmax Priors http://arxiv.org/pdf/2111.05307v1 Machine-learning custom-made basis functions for partial differential equations http://arxiv.org/pdf/2111.05299v1 Can Information Flows Suggest Targets for Interventions in Neural Circuits? http://arxiv.org/pdf/2111.05292v1 Generalization in quantum machine learning from few training data http://arxiv.org/pdf/2111.05232v1 Learning Rates for Nonconvex Pairwise Learning http://arxiv.org/pdf/2111.05231v1 MLHarness: A Scalable Benchmarking System for MLCommons http://arxiv.org/pdf/2111.05218v1 A research framework for writing differentiable PDE discretizations in JAX http://arxiv.org/pdf/2111.04941v1 Solving PDE-constrained Control Problems using Operator Learning http://arxiv.org/pdf/2111.04936v1 An Interactive Visualization Tool for Understanding Active Learning http://arxiv.org/pdf/2111.04933v1 DSBERT:Unsupervised Dialogue Structure learning with BERT http://arxiv.org/pdf/2111.05076v1 An Application of Quantum Machine Learning on Quantum Correlated Systems: Quantum Convolutional Neural Network as a Classifier for Many-Body Wavefunctions from the Quantum Variational Eigensolver ./Link/2021-11-08 http://arxiv.org/pdf/2111.04727v1 Efficiently Learning Any One Hidden Layer ReLU Network From Queries http://arxiv.org/pdf/2111.04726v1 Estimating High Order Gradients of the Data Distribution by Denoising http://arxiv.org/pdf/2111.04614v1 Learning Filterbanks for End-to-End Acoustic Beamforming http://arxiv.org/pdf/2111.04389v1 Lattice gauge symmetry in neural networks http://arxiv.org/pdf/2111.04225v1 Representation Learning via Quantum Neural Tangent Kernels http://arxiv.org/pdf/2111.04207v1 Uncertainty Quantification in Neural Differential Equations http://arxiv.org/pdf/2111.04153v1 Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments http://arxiv.org/pdf/2111.04105v1 DQRE-SCnet: A novel hybrid approach for selecting users in Federated Learning with Deep-Q-Reinforcement Learning based on Spectral Clustering http://arxiv.org/pdf/2111.04071v1 DVS: Deep Visibility Series and its Application in Construction Cost Index Forecasting http://arxiv.org/pdf/2111.03972v1 Understanding Layer-wise Contributions in Deep Neural Networks through Spectral Analysis http://arxiv.org/pdf/2111.03794v1 Physics-Informed Neural Operator for Learning Partial Differential Equations http://arxiv.org/pdf/2111.03890v1 Demystifying Deep Learning Models for Retinal OCT Disease Classification using Explainable AI http://arxiv.org/pdf/2111.04684v1 Physics-informed neural networks for understanding shear migration of particles in viscous flow http://arxiv.org/pdf/2111.03914v1 A systematic approach for obtaining and modeling a nonlocal eddy diffusivity ./Link/2021-11-05 http://arxiv.org/pdf/2111.03577v1 Mixtures of Laplace Approximations for Improved Post-Hoc Uncertainty in Deep Learning http://arxiv.org/pdf/2111.03563v1 Machine Learning Product State Distributions from Initial Reactant States for a Reactive Atom-Diatom Collision System http://arxiv.org/pdf/2111.03555v1 AUTOKD: Automatic Knowledge Distillation Into A Student Architecture Family http://arxiv.org/pdf/2111.03396v1 FedLess: Secure and Scalable Federated Learning Using Serverless Computing http://arxiv.org/pdf/2111.03583v1 Data-driven discovery of dimensionless numbers and scaling laws from experimental measurements http://arxiv.org/pdf/2111.03126v1 Generative Adversarial Network for Probabilistic Forecast of Random Dynamical System http://arxiv.org/pdf/2111.03598v1 Quantum Algorithms for Unsupervised Machine Learning and Neural Networks http://arxiv.org/pdf/2111.03240v1 Succinct Description and Efficient Simulation of Non-Markovian Open Quantum Systems ./Link/2021-11-04 http://arxiv.org/pdf/2111.03016v1 Graph neural network initialisation of quantum approximate optimisation http://arxiv.org/pdf/2111.02949v1 Finite-Time Consensus Learning for Decentralized Optimization with Nonlinear Gossiping http://arxiv.org/pdf/2111.02922v1 Identifying nonlinear dynamical systems from multi-modal time series data http://arxiv.org/pdf/2111.02893v1 Symmetry-Aware Autoencoders: s-PCA and s-nlPCA http://arxiv.org/pdf/2111.02862v1 Parameterized Knowledge Transfer for Personalized Federated Learning http://arxiv.org/pdf/2111.02673v1 Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering http://arxiv.org/pdf/2111.02627v1 A Personalized Federated Learning Algorithm: an Application in Anomaly Detection http://arxiv.org/pdf/2111.02636v1 A control method for solving high-dimensional Hamiltonian systems through deep neural networks http://arxiv.org/pdf/2111.03022v1 Lipid domain coarsening and fluidity in multicomponent lipid vesicles: A continuum based model and its experimental validation http://arxiv.org/pdf/2111.02541v1 Asymptotic-Preserving Neural Networks for Multiscale Time-Dependent Linear Transport Equations http://arxiv.org/pdf/2111.02673v1 Recurrent Neural Network Training with Convex Loss and Regularization Functions by Extended Kalman Filtering http://arxiv.org/pdf/2111.02893v1 Symmetry-Aware Autoencoders: s-PCA and s-nlPCA http://arxiv.org/pdf/2111.03022v1 Lipid domain coarsening and fluidity in multicomponent lipid vesicles: A continuum based model and its experimental validation http://arxiv.org/pdf/2111.02541v1 Asymptotic-Preserving Neural Networks for Multiscale Time-Dependent Linear Transport Equations http://arxiv.org/pdf/2111.02978v1 Gradient descent globally solves average-case non-resonant physical design problems http://arxiv.org/pdf/2111.02636v1 A control method for solving high-dimensional Hamiltonian systems through deep neural networks http://arxiv.org/pdf/2111.03016v1 Graph neural network initialisation of quantum approximate optimisation http://arxiv.org/pdf/2111.02999v1 Quantum search-to-decision reductions and the state synthesis problem http://arxiv.org/pdf/2111.02951v1 Quantum tangent kernel http://arxiv.org/pdf/2111.02922v1 Identifying nonlinear dynamical systems from multi-modal time series data ./Link/2021-11-03 http://arxiv.org/pdf/2111.02169v1 Power Flow Balancing with Decentralized Graph Neural Networks http://arxiv.org/pdf/2111.01956v1 One Pass ImageNet http://arxiv.org/pdf/2111.02280v1 A reduced order Schwarz method for nonlinear multiscale elliptic equations based on two-layer neural networks http://arxiv.org/pdf/2111.02204v1 Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems ./Link/2021-11-02 http://arxiv.org/pdf/2111.01773v1 Data-Driven System Identification of 6-DoF Ship Motion in Waves with Neural Networks http://arxiv.org/pdf/2111.01564v1 MultiplexNet: Towards Fully Satisfied Logical Constraints in Neural Networks http://arxiv.org/pdf/2111.01555v1 Likelihood-Free Inference in State-Space Models with Unknown Dynamics http://arxiv.org/pdf/2111.01495v1 Constructing Neural Network-Based Models for Simulating Dynamical Systems http://arxiv.org/pdf/2111.01456v1 WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting http://arxiv.org/pdf/2111.01395v1 Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds http://arxiv.org/pdf/2111.01394v1 Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks http://arxiv.org/pdf/2111.01365v1 Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics http://arxiv.org/pdf/2111.01366v1 Improved Loss Function-Based Prediction Method of Extreme Temperatures in Greenhouses ./Link/2021-11-01 http://arxiv.org/pdf/2111.01103v1 Predicting Power System Dynamics and Transients: A Frequency Domain Approach http://arxiv.org/pdf/2111.00945v1 Escaping the abstraction: a foreign function interface for the Unified Form Language [UFL] http://arxiv.org/pdf/2111.00260v1 A Machine Learning approach to enhance the SUPG stabilization method for advection-dominated differential problems http://arxiv.org/pdf/2111.00217v1 On quadrature rules for solving Partial Differential Equations using Neural Networks http://arxiv.org/pdf/2111.00720v1 Validation of Immersed Boundary Simulations of Heart Valve Hemodynamics against In Vitro 4D Flow MRI Data http://arxiv.org/pdf/2111.00851v1 Quantum computing enhanced machine learning for physico-chemical applications ./Link/2021-10-29 http://arxiv.org/pdf/2110.15949v1 Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains http://arxiv.org/pdf/2110.15911v1 Physics-informed linear regression is a competitive approach compared to Machine Learning methods in building MPC http://arxiv.org/pdf/2110.15832v1 CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method http://arxiv.org/pdf/2110.15829v1 Holistic Deep Learning http://arxiv.org/pdf/2110.15739v1 Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation http://arxiv.org/pdf/2110.15667v1 QDCNN: Quantum Dilated Convolutional Neural Network http://arxiv.org/pdf/2110.15503v1 A Pre-processing Method for Fairness in Ranking http://arxiv.org/pdf/2110.15600v1 Data Driven based Dynamic Correction Prediction Model for NOx Emission of Coal Fired Boiler http://arxiv.org/pdf/2110.15553v1 Data-driven Uncertainty Quantification in Computational Human Head Models ./Link/2021-10-28 http://arxiv.org/pdf/2110.15358v1 Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language http://arxiv.org/pdf/2110.15335v1 Bayesian Sequential Optimal Experimental Design for Nonlinear Models Using Policy Gradient Reinforcement Learning http://arxiv.org/pdf/2110.15305v1 Cooperative Deep $Q$-learning Framework for Environments Providing Image Feedback http://arxiv.org/pdf/2110.15288v1 Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction http://arxiv.org/pdf/2110.15245v1 From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence http://arxiv.org/pdf/2110.15200v1 Generating 3D Molecules Conditional on Receptor Binding Sites with Deep Generative Models http://arxiv.org/pdf/2110.15137v1 Learning Aggregations of Binary Activated Neural Networks with Probabilities over Representations http://arxiv.org/pdf/2110.15013v1 Deeptime: a Python library for machine learning dynamical models from time series data http://arxiv.org/pdf/2110.14937v1 Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT http://arxiv.org/pdf/2110.14856v1 An Operator Theoretic Perspective on Pruning Deep Neural Networks ./Link/2021-10-27 http://arxiv.org/pdf/2110.14583v1 Deep learning via message passing algorithms based on belief propagation http://arxiv.org/pdf/2110.14514v1 Streaming Generalized Canonical Polyadic Tensor Decompositions http://arxiv.org/pdf/2110.14402v1 Learning where to learn: Gradient sparsity in meta and continual learning http://arxiv.org/pdf/2110.14343v1 Comprehensive learning particle swarm optimization enabled modeling framework for multi-step-ahead influenza prediction http://arxiv.org/pdf/2110.14296v1 Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems http://arxiv.org/pdf/2110.14286v1 TopicNet: Semantic Graph-Guided Topic Discovery http://arxiv.org/pdf/2110.14216v1 What Do We Mean by Generalization in Federated Learning? http://arxiv.org/pdf/2110.14157v1 Dream to Explore: Adaptive Simulations for Autonomous Systems http://arxiv.org/pdf/2110.14121v1 On Computing the Hyperparameter of Extreme Learning Machines: Algorithm and Application to Computational PDEs, and Comparison with Classical and High-Order Finite Elements http://arxiv.org/pdf/2110.14392v1 Taylor Swift: Taylor Driven Temporal Modeling for Swift Future Frame Prediction http://arxiv.org/pdf/2110.14144v1 Physically Explainable CNN for SAR Image Classification http://arxiv.org/pdf/2110.14374v1 A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization ./Link/2021-10-26 http://arxiv.org/pdf/2110.13786v1 Diversity and Generalization in Neural Network Ensembles http://arxiv.org/pdf/2110.13680v1 Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN http://arxiv.org/pdf/2110.13638v1 EDLaaS; Fully Homomorphic Encryption Over Neural Network Graphs http://arxiv.org/pdf/2110.13585v1 Concepts for Automated Machine Learning in Smart Grid Applications http://arxiv.org/pdf/2110.13530v1 An extended physics informed neural network for preliminary analysis of parametric optimal control problems http://arxiv.org/pdf/2110.13511v1 AutoDEUQ: Automated Deep Ensemble with Uncertainty Quantification http://arxiv.org/pdf/2110.13440v1 A deep learning driven pseudospectral PCE based FFT homogenization algorithm for complex microstructures http://arxiv.org/pdf/2110.13361v1 Physics-Informed Neural Networks (PINNs) for Parameterized PDEs: A Metalearning Approach http://arxiv.org/pdf/2110.13344v1 Sinusoidal Flow: A Fast Invertible Autoregressive Flow http://arxiv.org/pdf/2110.13330v1 Robust Learning of Physics Informed Neural Networks http://arxiv.org/pdf/2110.13720v1 Deep DIC: Deep Learning-Based Digital Image Correlation for End-to-End Displacement and Strain Measurement http://arxiv.org/pdf/2110.13481v1 Efficient 6D Vlasov simulation using the dynamical low-rank framework Ensign ./Link/2021-10-25 http://arxiv.org/pdf/2110.13100v1 Parameter Prediction for Unseen Deep Architectures http://arxiv.org/pdf/2110.13041v1 Applications and Techniques for Fast Machine Learning in Science http://arxiv.org/pdf/2110.13040v1 Neural Flows: Efficient Alternative to Neural ODEs http://arxiv.org/pdf/2110.12976v1 Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks http://arxiv.org/pdf/2110.12951v1 Seeing biodiversity: perspectives in machine learning for wildlife conservation http://arxiv.org/pdf/2110.12773v1 Scientific Machine Learning Benchmarks http://arxiv.org/pdf/2110.12690v1 Scalable Lipschitz Residual Networks with Convex Potential Flows http://arxiv.org/pdf/2110.12612v1 DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021 http://arxiv.org/pdf/2110.12484v1 Micro Batch Streaming: Allowing the Training of DNN models Using a large batch size on Small Memory Systems http://arxiv.org/pdf/2110.12367v1 Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties http://arxiv.org/pdf/2110.13100v1 Parameter Prediction for Unseen Deep Architectures http://arxiv.org/pdf/2110.13041v1 Applications and Techniques for Fast Machine Learning in Science http://arxiv.org/pdf/2110.13040v1 Neural Flows: Efficient Alternative to Neural ODEs http://arxiv.org/pdf/2110.12976v1 Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks http://arxiv.org/pdf/2110.12951v1 Seeing biodiversity: perspectives in machine learning for wildlife conservation http://arxiv.org/pdf/2110.12773v1 Scientific Machine Learning Benchmarks http://arxiv.org/pdf/2110.12690v1 Scalable Lipschitz Residual Networks with Convex Potential Flows http://arxiv.org/pdf/2110.12612v1 DelightfulTTS: The Microsoft Speech Synthesis System for Blizzard Challenge 2021 http://arxiv.org/pdf/2110.12484v1 Micro Batch Streaming: Allowing the Training of DNN models Using a large batch size on Small Memory Systems http://arxiv.org/pdf/2110.12367v1 Deep Learning for Simultaneous Inference of Hydraulic and Transport Properties http://arxiv.org/pdf/2110.12308v1 A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness http://arxiv.org/pdf/2110.12981v1 Neural ODE and DAE Modules for Power System Dynamic Modeling http://arxiv.org/pdf/2110.12925v1 CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning http://arxiv.org/pdf/2110.12352v1 DiffSRL: Learning Dynamic-aware State Representation for Deformable Object Control with Differentiable Simulator http://arxiv.org/pdf/2110.12990v1 A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations http://arxiv.org/pdf/2110.13020v1 Deterministic particle flows for constraining SDEs http://arxiv.org/pdf/2110.12281v1 On Seven Fundamental Optimization Challenges in Machine Learning http://arxiv.org/pdf/2110.12522v1 An efficient estimation of time-varying parameters of dynamic models by combining offline batch optimization and online data assimilation ./Link/2021-10-23 http://arxiv.org/pdf/2110.12119v1 Dynamics Near the Three-Body Libration Points via Koopman Operator Theory ./Link/2021-10-22 http://arxiv.org/pdf/2110.11950v1 Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff http://arxiv.org/pdf/2110.11875v1 GeneDisco: A Benchmark for Experimental Design in Drug Discovery http://arxiv.org/pdf/2110.11854v1 Using scientific machine learning for experimental bifurcation analysis of dynamic systems http://arxiv.org/pdf/2110.11826v1 Predictive machine learning for prescriptive applications: a coupled training-validating approach http://arxiv.org/pdf/2110.11678v1 DQC: a Python program package for Differentiable Quantum Chemistry http://arxiv.org/pdf/2110.11592v1 Learning Text-Image Joint Embedding for Efficient Cross-Modal Retrieval with Deep Feature Engineering http://arxiv.org/pdf/2110.11531v1 Fractional Modeling in Action: A Survey of Nonlocal Models for Subsurface Transport, Turbulent Flows, and Anomalous Materials http://arxiv.org/pdf/2110.11735v1 Kernel-based models for system analysis http://arxiv.org/pdf/2110.11528v1 Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow ./Link/2021-10-21 http://arxiv.org/pdf/2110.11286v1 One-Shot Transfer Learning of Physics-Informed Neural Networks http://arxiv.org/pdf/2110.11265v1 Deep Reinforcement Learning for Online Control of Stochastic Partial Differential Equations http://arxiv.org/pdf/2110.10972v1 Sliced-Wasserstein Gradient Flows http://arxiv.org/pdf/2110.10895v1 Finite Volume Least-Squares Neural Network (FV-LSNN) Method for Scalar Nonlinear Hyperbolic Conservation Laws http://arxiv.org/pdf/2110.10863v1 Deep Generative Models in Engineering Design: A Review http://arxiv.org/pdf/2110.10833v1 High-resolution rainfall-runoff modeling using graph neural network http://arxiv.org/pdf/2110.10802v1 A Data-Centric Optimization Framework for Machine Learning http://arxiv.org/pdf/2110.10724v1 Semi-supervised physics guided DL framework for predicting the I-V characteristics of GAN HEMT http://arxiv.org/pdf/2110.10721v1 Learning quantum dynamics with latent neural ODEs http://arxiv.org/pdf/2110.10710v1 Stochastic Learning Rate Optimization in the Stochastic Approximation and Online Learning Settings http://arxiv.org/pdf/2110.10461v1 Scalable One-Pass Optimisation of High-Dimensional Weight-Update Hyperparameters by Implicit Differentiation http://arxiv.org/pdf/2110.10441v1 Feedback Linearization of Car Dynamics for Racing via Reinforcement Learning http://arxiv.org/pdf/2110.10391v1 Robust lEarned Shrinkage-Thresholding (REST): Robust unrolling for sparse recover http://arxiv.org/pdf/2110.10249v1 Neural Stochastic Partial Differential Equations http://arxiv.org/pdf/2110.10234v1 More Engineering, No Silos: Rethinking Processes and Interfaces in Collaboration between Interdisciplinary Teams for Machine Learning Projects http://arxiv.org/pdf/2110.10220v1 Patch Based Transformation for Minimum Variance Beamformer Image Approximation Using Delay and Sum Pipeline http://arxiv.org/pdf/2110.09916v1 Identification of high order closure terms from fully kinetic simulations using machine learning http://arxiv.org/pdf/2110.09823v2 An Empirical Study: Extensive Deep Temporal Point Process http://arxiv.org/pdf/2110.09813v1 Multi-Objective Loss Balancing for Physics-Informed Deep Learning http://arxiv.org/pdf/2110.09770v1 AEFE: Automatic Embedded Feature Engineering for Categorical Features http://arxiv.org/pdf/2110.09658v1 System Norm Regularization Methods for Koopman Operator Approximation http://arxiv.org/pdf/2110.09647v1 Relational Neural Markov Random Fields http://arxiv.org/pdf/2110.09455v1 TLDR: Twin Learning for Dimensionality Reduction http://arxiv.org/pdf/2110.09443v1 Beltrami Flow and Neural Diffusion on Graphs http://arxiv.org/pdf/2110.09361v1 Efficient Exploration in Binary and Preferential Bayesian Optimization http://arxiv.org/pdf/2110.09360v1 Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning http://arxiv.org/pdf/2110.09326v1 Neural message passing for predicting abnormal grain growth in Monte Carlo simulations of microstructural evolution http://arxiv.org/pdf/2110.09163v1 A Dimensionality Reduction Approach for Convolutional Neural Networks http://arxiv.org/pdf/2110.09001v1 Deep Learning-Based Power Control for Uplink Cell-Free Massive MIMO Systems http://arxiv.org/pdf/2110.08902v1 Green Simulation Assisted Policy Gradient to Accelerate Stochastic Process Control http://arxiv.org/pdf/2110.08725v1 A Riemannian Mean Field Formulation for Two-layer Neural Networks with Batch Normalization http://arxiv.org/pdf/2110.08712v1 Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models http://arxiv.org/pdf/2110.08689v1 Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks http://arxiv.org/pdf/2110.08649v1 Equivariant Discrete Normalizing Flows http://arxiv.org/pdf/2110.08611v1 Deep Active Learning by Leveraging Training Dynamics http://arxiv.org/pdf/2110.08607v1 Physics-guided Deep Markov Models for Learning Nonlinear Dynamical Systems with Uncertainty http://arxiv.org/pdf/2110.08557v2 DPNAS: Neural Architecture Search for Deep Learning with Differential Privacy http://arxiv.org/pdf/2110.08529v1 Sharpness-Aware Minimization Improves Language Model Generalization http://arxiv.org/pdf/2110.11070v1 A Nested Weighted Tchebycheff Multi-Objective Bayesian Optimization Approach for Flexibility of Unknown Utopia Estimation in Expensive Black-box Design Problems http://arxiv.org/pdf/2110.09131v1 Ensembling Graph Predictions for AMR Parsing http://arxiv.org/pdf/2110.08975v1 Deep Transfer Learning & Beyond: Transformer Language Models in Information Systems Research http://arxiv.org/pdf/2110.10093v1 Stochastic Primal-Dual Deep Unrolling Networks for Imaging Inverse Problems http://arxiv.org/pdf/2110.09021v1 Artificial Neural Network and Its Application Research Progress in Chemical Process http://arxiv.org/pdf/2110.09155v1 A dynamic mode decomposition extension for the forecasting of parametric dynamical systems http://arxiv.org/pdf/2110.10775v1 Reduced Basis Approximations of Parameterized Dynamical Partial Differential Equations via Neural Networks http://arxiv.org/pdf/2110.09218v1 Neural-network learning of SPOD latent dynamics http://arxiv.org/pdf/2110.08442v1 Koopman Operator Theory for Nonlinear Dynamic Modeling using Dynamic Mode Decomposition