I’m Wei Wen, a Ph.D. candidate in Duke University, supervised by Dr. Hai Helen Li and Dr. Yiran Chen. My research is Machine Learning and its applications in Computer Vision and Natural Language Processing. Recently, I focus on understanding learning algorithms, structural learning for efficient deep learning, and optimization algorithms for distributed deep learning.
More specific, I am studying learning algorithms to escape sharp minima in deep neural networks to understand the generalization, TernGrad SGD to overcome the communication bottleneck in distributed machine learning, and structural leaning algorithms to learn sparse and low-rank structures in deep neural networks for faster inference.
My Ph.D. thesis is “Efficient and Scalable Deep Learning”. Dissertation committee: Hai Helen Li, Yiran Chen, Robert Calderbank, Yangqing Jia and Guillermo Sapiro.
I had internship in Facebook Research, Microsoft Research Redmond & Asia, and HP Labs, where I incorporated my research into industrial AI productions.
Industrial Experience
More in LinkedIn.
Facebook Research – AI Infra & Applied Machine Learning, Menlo Park, CA, USA, 05/2018-08/2018
Research Intern, Mentor: Yangqing Jia
– Caffe2 and Personalization
– Distributed Machine Learning
Microsoft Research – Web Search and AI, Redmond & Bellevue, WA, USA, 05/2017-07/2017
Research Intern, Mentors: Yuxiong He & Fang Liu
– Machine Reading Comprehension
– Recurrent Neural Nets
HP Labs – Platform Architecture Group, Palo Alto, CA, USA, 05/31/2016-08/31/2016
Research Intern, Mentor: Cong Xu. Manager: Paolo Faraboschi
– Worked on distributed deep learning.
Agricultural Bank of China – Software Development Center, Beijing, 07/2013-07/2014
Software Engineer
– Developed web services for online bank transactions.
Microsoft Research – Mobile and Sensing Systems Group, Beijing, China, 04/2013-06/2013
Research Intern
– Worked on mobile computer vision
Tencent Inc. – Advertising Platform and Products Division, Beijing, China, 07/2012-09/2012
Software Engineer Intern.
Publications
- Wei Wen, Yandan Wang, Feng Yan, Cong Xu, Yiran Chen, Hai Li, “SmoothOut: Smoothing Out Sharp Minima to Improve Generalization in Deep Learning”, preprint. [paper][code]
- Wei Wen, Yuxiong He, Samyam Rajbhandari, Minjia Zhang, Wenhan Wang, Fang Liu, Bin Hu, Yiran Chen, Hai Li, “Learning Intrinsic Sparse Structures within Long Short-Term Memory”,the 6th International Conference on Learning Representations (ICLR), 2018. [poster][paper][code]
- Wei Wen, Cong Xu, Feng Yan, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li, “TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning”,the 31st Annual Conference on Neural Information Processing Systems (NIPS), 2017. (Oral, 40/3240=1.2%. Available in PyTorch/Caffe2.). [paper][video][slides][code][poster]
- Wei Wen, Cong Xu, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li, “Coordinating Filters for Faster Deep Neural Networks”, Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2017. [paper][code][poster]
- Wei Wen, Chunpeng Wu, Yandan Wang, Yiran Chen, Hai Li, “Learning Structured Sparsity in Deep Neural Networks”, the 30th Annual Conference on Neural Information Processing Systems (NIPS), 2016. Acceptance Rate: 568/2500=22.7%. (Integrated into Intel Nervada) [paper][code][poster]
- Hsin-Pai Cheng, Yuanjun Huang, Xuyang Guo, Feng Yan, Yifei Huang, Wei Wen, Hai Li, Yiran Chen, “Differentiable Fine-grained Quantization for Deep Neural Network Compression”, NeurIPS 2018 CDNNRIA Workshop . [paper]
- Jongsoo Park, Sheng Li, Wei Wen, Ping Tak Peter Tang, Hai Li, Yiran Chen, Pradeep Dubey, “Faster CNNs with Direct Sparse Convolutions and Guided Pruning”, the 5th International Conference on Learning Representations (ICLR), 2017. [paper][code][media]
- Chunpeng Wu, Wei Wen, , Yiran Chen,Hai Li, “A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation”, CVPR, 2017. [paper]
- Yandan Wang, Wei Wen, Linghao Song, Hai Li, “Classification Accuracy Improvement for Neuromorphic Computing Systems with One-level Precision Synapses “, ASP-DAC, 2017. (Best Paper Award). [paper]
- Wei Wen, Chunpeng Wu, Yandan Wang, Kent Nixon, Qing Wu, Mark Barnell, Hai Li, Yiran Chen, “A New Learning Method for Inference Accuracy, Core Occupation, and Performance Co-optimization on TrueNorth Chip”, 53rd ACM/EDAC/IEEE Design Automation Conference (DAC), 2016. Acceptance Rate: 152/876=17.4%. (Best Paper Nomination, 16/876=1.83%). [paper]
- Wei Wen, Chi-Ruo Wu, Xiaofang Hu, Beiye Liu, Tsung-Yi Ho, Xin Li, Yiran Chen, “An EDA Framework for Large Scale Hybrid Neuromorphic Computing Systems”, 52nd ACM/EDAC/IEEE Design Automation Conference (DAC), 2015. Acceptance Rate: 162/789=20.5%. (Best Paper Nomination, 7/789=0.89%). [paper]
- Yandan Wang, Wei Wen, Beiye Liu, Donald Chiarulli, Hai Li, “Group Scissor: Scaling Neuromorphic Computing Design to Big Neural Networks”, 54th ACM/EDAC/IEEE Design Automation Conference (DAC), 2017. Acceptance Rate: 24%. [paper]
- Jongsoo Park, Sheng R. Li, Wei Wen, Hai Li, Yiran Chen, Pradeep Dubey, “Holistic SparseCNN: Forging the Trident of Accuracy, Speed, and Size”, arXiv 1608.01409, 2016. (in Intel Developer Forum 2016, pages 41-43). [paper][code]
Talks and Presentations
- UC Berkeley, Scientific Computing and Matrix Computations Seminar, “On Matrix Sparsification and Quantization for Efficient and Scalable Deep Learning“, 10/10/2018
- Cornell University, AI Seminar, “Efficient and Scalable Deep Learning“, 10/05/2018
- NIPS 2017, TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning, 12/6/2017
- Alibaba DAMO Academy, “Deep Learning in Cloud-Edge AI Systems“, SunnyVale, CA, 06/28/2018
- “Deep Learning in the Cloud and in the Fog”, [Blog@AI科技评论]
- “Deep Learning in Cloud-Edge AI Systems”, [Video in Mandarin @将门创投]
- “Lifting Efficiency in Deep Learning – For both Training and Inference”, [Video in Mandarin @机器之心]
- “Scalable Event-driven Neuromorphic Learning Machines 3″, Intel Strategic Research Alliances (ISRA) – UC Berkeley, UC Irvine, Univ of Pitt, UCSD”, 10/27/2016
- “A Predictive Performance Model of Distributed Deep Learning on Heterogeneous Systems”, Final Intern Talk, HP Labs, 08/23/2016
- “Variation-Aware Predictive Performance Model for Distributed Deep Learning”, Summer Intern Fair Poster, HP Labs, 08/02/2016
- “An Overview of Deep Learning Accelerator”, Seminar, HP Labs, 07/18/2016
Activities
- Serving as a reviewer of NIPS, ICLR, CVPR, TNNLS, TCAD, Neurocomputing, ICME, etc
- Activity volunteer, Machine Learning for Girls, FEMMES (Female Excelling More in Math, Engineering, and Science) Capstone at Duke University, 02/2018
- Conference volunteer, ESWEEK 2016, OCTOBER 2-7, PITTSBURGH, PA, USA, 10/2016
Teaching
- TA: CEE 690/ECE 590: Introduction to Deep Learning, Fall 2018
Education
08/2017-12/2019 (Estimated)
Ph.D. in Electrical and Computer Engineering, Duke University, Durham, NC, United States
09/2014-08/2017 (Transferred to Duke University)
Ph.D. in Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, United States
09/2010-01/2013
Master in Electronic and Information Engineering, Beihang University, Beijing, China
09/2006-07/2010
Bachelor in Electronic and Information Engineering, Beihang University, Beijing, China