My research interest is large scale machine learning, particularly on scalable deep generative models and deep topic models. Previously, I focused on scaling up various topic models, including LDA, CTM, DTM, etc.


Publications

Google Scholar Profile
Stochastic Training of Graph Convolutional Networks
Jianfei Chen and Jun Zhu
arXiv:1710.10568
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
Towards Training Probabilistic Topic Models on Neuromorphic Multi-chip Systems
Zihao Xiao, Jun Zhu, and Jianfei Chen
To appear in 32nd AAAI Conference on Artificial Intelligence (AAAI), New Orleans, USA, 2018.
Population Matching Discrepancy and Applications in Deep Learning
Jianfei Chen, Chongxuan Li, Yizhong Ru and Jun Zhu
To Appear in Advances in Neural Information Processing Systems (NIPS), Long Beach, CA, 2017
Scalable Inference for Nested Chinese Restaurant Process Topic Models
Jianfei Chen, Jun Zhu, Jie Lu and Shixia Liu
Submitted to Very Large Data Bases (VLDB), 2018.
Big Learning with Bayesian Methods
Jun Zhu, Jianfei Chen, and Wenbo Hu
National Science Review (NSR), nwx044. doi: 10.1093/nsr/nwx044, (arXiv:1411.6370), 2017
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.
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. [Code]
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 (SIGKDD 2016).
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 (in press), 2016. (TVCG 2016)
Scaling up Dynamic Topic Models
Arnab Bhadury, Jianfei Chen, Jun Zhu, and Shixia Liu
World Wide Web Conference (WWW), Montreal, Canada, 2016. (WWW 2016)
Dropout Training for SVMs with Data Augmentation
Ning Chen, Jun Zhu, Jianfei Chen, and Ting Chen
arXiv 1508.02268, 2015.
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 (IEEE VIS 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 (ICML 2014)
Dropout Training for Support Vector Machines
Ning Chen, Jun Zhu, Jianfei Chen and Bo Zhang
Association for the Advancement of Artificial Intelligence (AAAI), 2014
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[Homepage][Slide][Code][NIPS Poster]

Honors and Awards

09/2013 Scholarship on comprehensive merit
09/2012 National scholarship
06/2012 2nd place of Programming Competition between THU, NTHU and HKUST
04/2012 Third prize of Tsinghua Challenge Cup
09/2011 Scholarship on comprehensive merit
07/2011 Excellent volunteer for Tsinghua English Summer Camp
04/2011 Champion of 15th Tsinghua AI Competition
10/2010 Silver Award of ACM/ICPC 2010 Asia Regional, Chengdu
09/2010 Scholarship for distinguished freshmen
08/2010 5th place of Baidu Astar 2010
08/2009 Gold medal of National Olympiad of Informatics

Miscellaneous

This is a reading list for topic models.

Acknowledgement

This webpage design was "stolen" from Peiyun Hu. Special thanks to him!


Last updated on Nov. 24th, 2017.