Dehao Chen

Publication Activity (10 Years)

Years Active: 2006-2023
Publications (10 Years): 18

Top Topics

Training Process

Backpropagation Algorithm

Pseudo Feedback

Top Venues

IEEE Signal Process. Lett.

Publications

Huisheng Zhang, Zaiqiang Wang, Dehao Chen, Shuai Zhu, Dongpo Xu
Quaternion Extreme Learning Machine Based on Real Augmented Representation. IEEE Signal Process. Lett. 30 (2023)
Shibo Wang, Jinliang Wei, Amit Sabne, Andy Davis, Berkin Ilbeyi, Blake Hechtman, Dehao Chen, Karthik Srinivasa Murthy, Marcello Maggioni, Qiao Zhang, Sameer Kumar, Tongfei Guo, Yuanzhong Xu, Zongwei Zhou
Overlap Communication with Dependent Computation via Decomposition in Large Deep Learning Models. ASPLOS (1) (2023)
Lei Ren, Dehao Chen, Zexin Miao, Huisheng Zhang
News Recommendation Model Based on Long-Term and Short-Term Interests. ICBDT (2022)
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, YaGuang Li, Hongrae Lee, Huaixiu Steven Zheng, Amin Ghafouri, Marcelo Menegali, Yanping Huang, Maxim Krikun, Dmitry Lepikhin, James Qin, Dehao Chen, Yuanzhong Xu, Zhifeng Chen, Adam Roberts, Maarten Bosma, Yanqi Zhou, Chung-Ching Chang, Igor Krivokon, Will Rusch, Marc Pickett, Kathleen S. Meier-Hellstern, Meredith Ringel Morris, Tulsee Doshi, Renelito Delos Santos, Toju Duke, Johnny Soraker, Ben Zevenbergen, Vinodkumar Prabhakaran, Mark Diaz, Ben Hutchinson, Kristen Olson, Alejandra Molina, Erin Hoffman-John, Josh Lee, Lora Aroyo, Ravi Rajakumar, Alena Butryna, Matthew Lamm, Viktoriya Kuzmina, Joe Fenton, Aaron Cohen, Rachel Bernstein, Ray Kurzweil, Blaise Aguera-Arcas, Claire Cui, Marian Croak, Ed Chi, Quoc Le
LaMDA: Language Models for Dialog Applications. CoRR (2022)
Sameer Kumar, Yu Emma Wang, Cliff Young, James Bradbury, Naveen Kumar, Dehao Chen, Andy Swing
Exploring the Limits of Concurrency in ML Training on Google TPUS. MLSys (2021)
Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Blake A. Hechtman, Yanping Huang, Rahul Joshi, Maxim Krikun, Dmitry Lepikhin, Andy Ly, Marcello Maggioni, Ruoming Pang, Noam Shazeer, Shibo Wang, Tao Wang, Yonghui Wu, Zhifeng Chen
GSPMD: General and Scalable Parallelization for ML Computation Graphs. CoRR (2021)
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. ICLR (2021)
Sameer Kumar, James Bradbury, Cliff Young, Yu Emma Wang, Anselm Levskaya, Blake A. Hechtman, Dehao Chen, HyoukJoong Lee, Mehmet Deveci, Naveen Kumar, Pankaj Kanwar, Shibo Wang, Skye Wanderman-Milne, Steve Lacy, Tao Wang, Tayo Oguntebi, Yazhou Zu, Yuanzhong Xu, Andy Swing
Exploring the limits of Concurrency in ML Training on Google TPUs. CoRR (2020)
Dmitry Lepikhin, HyoukJoong Lee, Yuanzhong Xu, Dehao Chen, Orhan Firat, Yanping Huang, Maxim Krikun, Noam Shazeer, Zhifeng Chen
GShard: Scaling Giant Models with Conditional Computation and Automatic Sharding. CoRR (2020)
Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Hongjun Choi, Blake A. Hechtman, Shibo Wang
Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training. CoRR (2020)
Peter Mattson, Christine Cheng, Gregory F. Diamos, Cody Coleman, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debo Dutta, Udit Gupta, Kim M. Hazelwood, Andy Hock, Xinyuan Huang, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
MLPerf Training Benchmark. MLSys (2020)
Peter Mattson, Christine Cheng, Cody Coleman, Greg Diamos, Paulius Micikevicius, David A. Patterson, Hanlin Tang, Gu-Yeon Wei, Peter Bailis, Victor Bittorf, David Brooks, Dehao Chen, Debojyoti Dutta, Udit Gupta, Kim M. Hazelwood, Andrew Hock, Xinyuan Huang, Bill Jia, Daniel Kang, David Kanter, Naveen Kumar, Jeffery Liao, Guokai Ma, Deepak Narayanan, Tayo Oguntebi, Gennady Pekhimenko, Lillian Pentecost, Vijay Janapa Reddi, Taylor Robie, Tom St. John, Carole-Jean Wu, Lingjie Xu, Cliff Young, Matei Zaharia
MLPerf Training Benchmark. CoRR (2019)
Yanping Huang, Youlong Cheng, Ankur Bapna, Orhan Firat, Dehao Chen, Mia Xu Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Yonghui Wu, Zhifeng Chen
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. NeurIPS (2019)
Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia Xu Chen, Ye Jia, Anjuli Kannan, Tara N. Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George F. Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel Bacchiani, Thomas B. Jablin, Robert Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon
Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling. CoRR (2019)
Sameer Kumar, Victor Bitorff, Dehao Chen, Chiachen Chou, Blake A. Hechtman, HyoukJoong Lee, Naveen Kumar, Peter Mattson, Shibo Wang, Tao Wang, Yuanzhong Xu, Zongwei Zhou
Scale MLPerf-0.6 models on Google TPU-v3 Pods. CoRR (2019)
Chris Ying, Sameer Kumar, Dehao Chen, Tao Wang, Youlong Cheng
Image Classification at Supercomputer Scale. CoRR (2018)
Yanping Huang, Yonglong Cheng, Dehao Chen, HyoukJoong Lee, Jiquan Ngiam, Quoc V. Le, Zhifeng Chen
GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism. CoRR (2018)
Dehao Chen, David Xinliang Li, Tipp Moseley
AutoFDO: automatic feedback-directed optimization for warehouse-scale applications. CGO (2016)
Baptiste Wicht, Roberto A. Vitillo, Dehao Chen, David Levinthal
Hardware Counted Profile-Guided Optimization. CoRR (2014)
Dehao Chen, Neil Vachharajani, Robert Hundt, Xinliang D. Li, Stéphane Eranian, Wenguang Chen, Weimin Zheng
Taming Hardware Event Samples for Precise and Versatile Feedback Directed Optimizations. IEEE Trans. Computers 62 (2) (2013)
Dehao Chen, Wenguang Chen, Weimin Zheng
CUDA-Zero: a framework for porting shared memory GPU applications to multi-GPUs. Sci. China Inf. Sci. 55 (3) (2012)
Chun-Tao Hong, Dehao Chen, Yu-Bei Chen, Wen-Guang Chen, Wei-Min Zheng, Haibo Lin
Providing Source Code Level Portability Between CPU and GPU with MapCG. J. Comput. Sci. Technol. 27 (1) (2012)
Chuntao Hong, Dehao Chen, Wenguang Chen, Weimin Zheng, Haibo Lin
MapCG: writing parallel program portable between CPU and GPU. PACT (2010)
Dehao Chen, Neil Vachharajani, Robert Hundt, Shih-wei Liao, Vinodha Ramasamy, Paul Yuan, Wenguang Chen, Weimin Zheng
Taming hardware event samples for FDO compilation. CGO (2010)
Dehao Chen, Chunrong Lai, Wei Hu, Wenguang Chen, Yimin Zhang, Weimin Zheng
Tree partition based parallel frequent pattern mining on shared memory systems. IPDPS (2006)