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2020 – today
- 2023
[i7]Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang:
Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search. CoRR abs/2302.01382 (2023)- [i6]Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang, Chiachen Chou, Tom Jablin, Jian Li, Elfie Guo, Caitlin Stanton, Siddharth Joshi, Yu Emma Wang:
Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization. CoRR abs/2306.04879 (2023) - [i5]Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap, Mark James Horeni, Yu Emma Wang, Juan Ye, Siddharth Joshi:
Hadamard Domain Training with Integers for Class Incremental Quantized Learning. CoRR abs/2310.03675 (2023) - 2022
- [c4]Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten P. Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathleen S. Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui:
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. ICML 2022: 5547-5569 - [i4]Xiaofan Zhang, Zongwei Zhou, Deming Chen, Yu Emma Wang:
AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models. CoRR abs/2201.08539 (2022) - 2021
- [j1]Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks:
Exploiting Parallelism Opportunities with Deep Learning Frameworks. ACM Trans. Archit. Code Optim. 18(1): 9:1-9:23 (2021) - [c3]Phitchaya Mangpo Phothilimthana, Amit Sabne, Nikhil Sarda, Karthik Srinivasa Murthy, Yanqi Zhou, Christof Angermueller, Mike Burrows, Sudip Roy, Ketan Mandke, Rezsa Farahani, Yu Emma Wang, Berkin Ilbeyi, Blake A. Hechtman, Bjarke Roune, Shen Wang, Yuanzhong Xu, Samuel J. Kaufman:
A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers. PACT 2021: 1-16 - [c2]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 - [i3]Nan Du, Yanping Huang, Andrew M. Dai, Simon Tong, Dmitry Lepikhin, Yuanzhong Xu, Maxim Krikun, Yanqi Zhou, Adams Wei Yu, Orhan Firat, Barret Zoph, Liam Fedus, Maarten Bosma, Zongwei Zhou, Tao Wang, Yu Emma Wang, Kellie Webster, Marie Pellat, Kevin Robinson, Kathy Meier-Hellstern, Toju Duke, Lucas Dixon, Kun Zhang, Quoc V. Le, Yonghui Wu, Zhifeng Chen, Claire Cui:
GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. CoRR abs/2112.06905 (2021) - 2020
- [i2]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 abs/2011.03641 (2020)
2010 – 2019
- 2019
- [c1]Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, David Brooks:
Demystifying Bayesian Inference Workloads. ISPASS 2019: 177-189 - [i1]Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks:
Exploiting Parallelism Opportunities with Deep Learning Frameworks. CoRR abs/1908.04705 (2019)
Coauthor Index
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