Publications

Google Scholar

2022

  • DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps
    Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, Jun Zhu
    Neural Information Processing Systems (NeurIPS), 2022 (arXiv, GitHub)

  • GACT: Activation Compressed Training for Generic Network Architectures
    Xiaoxuan Liu, Lianmin Zheng, Dequan Wang, Yukuo Cen, Weize Chen, Xu Han
    Jianfei Chen#, Zhiyuan Liu, Jie Tang, Joseph E. Gonzalez, Michael W. Mahoney, and Alvin Cheung
    International Conference on Machine Learning (ICML), 2022 (pdf, arXiv, GitHub)

  • Maximum Likelihood Training for Score-based Diffusion ODEs by High Order Denoising Score Matching
    Cheng Lu, Kaiwen Zheng, Fan Bao, Chongxuan Li, Jianfei Chen#, Jun Zhu#
    International Conference on Machine Learning (ICML), 2022 (pdf, arXiv, GitHub)

  • Fast Lossless Neural Compression with Integer-Only Discrete Flows
    Siyu Wang, Jianfei Chen#, Chongxuan Li, Jun Zhu#, and Bo Zhang
    International Conference on Machine Learning (ICML), 2022 (pdf, arXiv)

2021

  • ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training
    Jianfei Chen*, Lianmin Zheng*, Zhewei Yao, Dequan Wang, Ion Stoica, Michael W. Mahoney, and Joseph E. Gonzalez
    International Conference on Machine Learning (ICML), 2021 (Long talk, Accept rate ~3%) (pdf, arXiv, GitHub)

  • Implicit Normalizing Flows
    Cheng Lu, Jianfei Chen, Chongxuan Li, Qiuhao Wang, and Jun Zhu
    International Conference on Learning Representations (ICLR), 2021 (Spotlight, Accept rate ~5.5%) (pdf, arXiv)

2020

  • A Statistical Framework for Low-bitwidth Training of Deep Neural Networks
    Jianfei Chen, Yu Gai, Zhewei Yao, Michael W. Mahoney, and Joseph E. Gonzalez
    Neural Information Processing Systems (NeurIPS), 2020 (pdf, arXiv, GitHub)

  • VFlow: More Expressive Generative Flows with Variational Data Augmentation
    Jianfei Chen, Cheng Lu, Biqi Chenli, Jun Zhu, and Tian Tian
    International Conference on Machine Learning (ICML), 2020 (pdf, arXiv, GitHub)

2019

  • Efficient Algorithms for Representation Learning (PhD Dissertation, in Chinese).
    Jianfei Chen.

  • Efficient Learning Algorithm for Maximum Entropy Discrimination Topic Models (in Chinese).
    Jianfei Chen and Jun Zhu.
    Pattern Recognition and Artificial Intelligence, 2019 Vol. 32 (8): 736-745 (pdf)

2018

  • Stochastic Expectation Maximization with Variance Reduction.
    Jianfei Chen, Jun Zhu, Yee Whye Teh, and Tong Zhang.
    Neural Information Processing System, Montreal, Canada, 2018 (NIPS 2018) (pdf, GitHub)

  • Stochastic Training of Graph Convolutional Networks with Variance Reduction.
    Jianfei Chen, Jun Zhu, and Le Song.
    International Conference on Machine Learning, Stockholm, Sweden, 2018 (ICML 2018) (pdf, arXiv, GitHub)

  • Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems.
    Zihao Xiao, Jun Zhu, and Jianfei Chen AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018. (pdf)

  • Scalable Inference for Hierarchical Topic Models.
    Jianfei Chen, Jun Zhu, Jie Lu and Shixia Liu.
    Very Large Data Bases (VLDB), Rio de Janeiro, Brazil, 2018. (pdf, arXiv)

2017

  • ZhuSuan: A Library for Bayesian Deep Learning.
    Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, and Yuhao Zhou.
    arXiv:1709.05870. (arXiv, GitHub)

  • Population Matching Discrepancy and Applications in Deep Learning.
    Jianfei Chen, Chongxuan Li, Yizhong Ru and Jun Zhu.
    Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, 2017. (pdf, GitHub)

  • Big Learning with Bayesian Methods.
    Jun Zhu, Jianfei Chen, and Wenbo Hu.
    National Science Review 4.4 (2017): 627-651. (pdf, arXiv)

  • SaberLDA: Sparsity-Aware Learning of Topic Models on GPUs.
    Kaiwei Li, Jianfei Chen, Wenguang Chen, and Jun Zhu.
    Architectural Support for Programming Languages and Operating Systems (ASPLOS), Xi'an, China, 2017. (saberlda, arXiv)

2016

  • WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation.
    Jianfei Chen, Kaiwei Li, Jun Zhu, and Wenguang Chen.
    Very Large Data Bases (VLDB), New Delhi, India, 2016. (pdf, arXiv, GitHub)

  • Distributing the Stochastic Gradient Sampler for Large-Scale LDA.
    Yuan Yang, Jianfei Chen, and Jun Zhu.
    In Proc. of SIGKDD Conference on on Knowledge Discovery and Data Mining (KDD), San Francisco, 2016. (pdf)

  • TopicPanorama: A Full Picture of Relevant Topics.
    Xiting Wang, Shixia Liu, Junlin Liu, Jianfei Chen, Jun Zhu, and Baining Guo.
    IEEE Transactions on Visualization and Computer Graphics, 2016. (pdf)

  • Scaling up Dynamic Topic Models.
    Arnab Bhadury, Jianfei Chen, Jun Zhu, and Shixia Liu.
    World Wide Web Conference (WWW), Montreal, Canada, 2016. (pdf, arXiv)

2015

  • Dropout Training for SVMs with Data Augmentation.
    Ning Chen, Jun Zhu, Jianfei Chen, and Ting Chen.
    Frontiers of Computer Science (2015): 1-20. (pdf, arXiv)

2014

  • TopicPanorama: a Full Picture of Relevant Topics.
    Shixia Liu, Xiting Wang, Jianfei Chen, Jun Zhu, and Baining Guo.
    Proc. of IEEE Visualization, Paris, France, 2014.

  • Bayesian Max-Margin Multitask Learning with Data Augmentation.
    Chengtao Li, Jun Zhu, and Jianfei Chen.
    In Proc. of International Conference on Machine Learning, Beijing, China, 2014. (pdf)

  • Dropout Training for Support Vector Machines.
    Ning Chen, Jun Zhu, Jianfei Chen and Bo Zhang.
    Association for the Advancement of Artificial Intelligence (AAAI), 2014. (pdf)

2013

  • Scabable Inference for Logistic-Normal Topic Models.
    Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng and Bo Zhang.
    Advances in Neural Information Processing Systems (NIPS), 2013. (pdf, GitHub, demo)