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Preserving Pre-trained Features Helps Calibrate Fine-tuned Language Models. Guande He, Jianfei Chen#, and Jun Zhu#. International Conference on Learning Representations (ICLR), 2023 (pdf)
Parameter-efficient fine-tuning of large-scale pre-trained language models. Ning Ding, Yujia Qin, Guang Yang, Fuchao Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang Zhiyuan Liu, Hai-Tao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juanzi Li, and Maosong Sun Nature Machine Intelligence, 2023 (pdf)
ZhuSuan: design and implementation of differentiable probabilistic programming libraries (in Chinese). Jiaxin Shi, Jianfei Chen, and Jun Zhu. Sci Sin Inform, 2022, 52: 804–821, doi: 10.1360/SSI-2021-0005 (paper)
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 (Oral, Accept rate ~1.7%) (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)
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)
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)
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)
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)
ZhuSuan: A Library for Bayesian Deep Learning. Jiaxin Shi, Jianfei Chen, Jun Zhu, Shengyang Sun, Yucen Luo, Yihong Gu, and Yuhao Zhou.
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.
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)
WarpLDA: a Cache Efficient O(1) Algorithm for Latent Dirichlet Allocation. Jianfei Chen, Kaiwei Li, Jun Zhu, and Wenguang Chen.
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.
Scaling up Dynamic Topic Models. Arnab Bhadury, Jianfei Chen, Jun Zhu, and Shixia Liu. World Wide Web Conference (WWW), Montreal, Canada, 2016. (pdf, arXiv)
Dropout Training for SVMs with Data Augmentation. Ning Chen, Jun Zhu, Jianfei Chen, and Ting Chen.
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.
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)
Scabable Inference for Logistic-Normal Topic Models. Jianfei Chen, Jun Zhu, Zi Wang, Xun Zheng and Bo Zhang.
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