Yu Emma Wang - RankMe

14 papers A* 1B 2Journal 9Unranked 2

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Machine Learning Fleet Efficiency: Analyzing and Optimizing Large-Scale Google TPU Systems with ML Productivity Goodput. CoRR Arissa Wongpanich, Tayo Oguntebi, Jose Baiocchi Paredes, Yu Emma Wang, Phitchaya Mangpo Phothilimthana, Ritwika Mitra, Zongwei Zhou, Naveen Kumar, Vijay Janapa Reddi
2024	—	`conf`	Hadamard Domain Training with Integers for Class Incremental Quantized Learning. CoLLAs Martin Schiemer, Clemens JS Schaefer, Mark James Horeni, Yu Emma Wang, Juan Ye, Siddharth Joshi
2023	J	`jnl`	Augmenting Hessians with Inter-Layer Dependencies for Mixed-Precision Post-Training Quantization. CoRR Clemens JS Schaefer, Navid Lambert-Shirzad, Xiaofan Zhang, Chiachen Chou, Tom Jablin, Jian Li, Elfie Guo, Caitlin Stanton, Siddharth Joshi, Yu Emma Wang
2023	J	`jnl`	Hadamard Domain Training with Integers for Class Incremental Quantized Learning. CoRR Martin Schiemer, Clemens JS Schaefer, Jayden Parker Vap, Mark James Horeni, Yu Emma Wang, Juan Ye, Siddharth Joshi
2023	J	`jnl`	Mixed Precision Post Training Quantization of Neural Networks with Sensitivity Guided Search. CoRR Clemens JS Schaefer, Elfie Guo, Caitlin Stanton, Xiaofan Zhang, Tom Jablin, Navid Lambert-Shirzad, Jian Li, Chiachen Chou, Siddharth Joshi, Yu Emma Wang
2022	J	`jnl`	AutoDistill: an End-to-End Framework to Explore and Distill Hardware-Efficient Language Models. CoRR Xiaofan Zhang, Zongwei Zhou, Deming Chen, Yu Emma Wang
2022	A*	`conf`	GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. ICML 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
2021	B	`conf`	A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers. PACT 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
2021	J	`jnl`	Exploiting Parallelism Opportunities with Deep Learning Frameworks. ACM Trans. Archit. Code Optim. Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks
2021	—	`conf`	Exploring the Limits of Concurrency in ML Training on Google TPUS. MLSys Sameer Kumar, Yu Emma Wang, Cliff Young, James Bradbury, Naveen Kumar, Dehao Chen, Andy Swing
2021	J	`jnl`	GLaM: Efficient Scaling of Language Models with Mixture-of-Experts. CoRR 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
2020	J	`jnl`	Exploring the limits of Concurrency in ML Training on Google TPUs. CoRR 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
2019	B	`conf`	Demystifying Bayesian Inference Workloads. ISPASS Yu Emma Wang, Yuhao Zhu, Glenn G. Ko, Brandon Reagen, Gu-Yeon Wei, David Brooks
2019	J	`jnl`	Exploiting Parallelism Opportunities with Deep Learning Frameworks. CoRR Yu Emma Wang, Carole-Jean Wu, Xiaodong Wang, Kim M. Hazelwood, David Brooks