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Maximilian Lam
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2020 – today
- 2024
- [c8]Maximilian Lam
, Jeff Johnson
GPU-based Private Information Retrieval for On-Device Machine Learning Inference. ASPLOS (1) 2024: 197-214 - 2023
- [j5]Swapnil Awasthi
GWAS quality score for evaluating associated regions in GWAS analyses. Bioinform. 39(1) (2023) - [i13]Maximilian Lam, Jeff Johnson, Wenjie Xiong, Kiwan Maeng, Udit Gupta, Yang Li, Liangzhen Lai, Ilias Leontiadis, Minsoo Rhu, Hsien-Hsin S. Lee, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks, G. Edward Suh:
GPU-based Private Information Retrieval for On-Device Machine Learning Inference. CoRR abs/2301.10904 (2023) - 2022
- [j4]Srivatsan Krishnan, Max Lam, Sharad Chitlangia, Zishen Wan, Gabriel Barth-Maron, Aleksandra Faust, Vijay Janapa Reddi:
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning. Trans. Mach. Learn. Res. 2022 (2022) - [i12]Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks:
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference. CoRR abs/2203.02833 (2022) - 2021
- [c7]Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi:
Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs. PACT 2021: 129-141 - [c6]Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher:
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. ICML 2021: 5959-5968 - [c5]Daniel Galvez, Greg Diamos, Juan Torres, Keith Achorn, Juan Felipe Cerón, Anjali Gopi, David Kanter, Max Lam, Mark Mazumder, Vijay Janapa Reddi:
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. NeurIPS Datasets and Benchmarks 2021 - [i11]Vijay Janapa Reddi, Brian Plancher, Susan Kennedy
Widening Access to Applied Machine Learning with TinyML. CoRR abs/2106.04008 (2021) - [i10]Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher:
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix. CoRR abs/2106.06089 (2021) - [i9]Daniel Galvez, Greg Diamos, Juan Ciro, Juan Felipe Cerón, Keith Achorn, Anjali Gopi, David Kanter, Maximilian Lam, Mark Mazumder, Vijay Janapa Reddi:
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. CoRR abs/2111.09344 (2021) - 2020
- [j3]Max Lam
RICOPILI: Rapid Imputation for COnsortias PIpeLIne. Bioinform. 36(3): 930-933 (2020) - [i8]Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi:
Quantized Neural Network Inference with Precision Batching. CoRR abs/2003.00822 (2020) - [i7]Colby R. Banbury, Vijay Janapa Reddi, Max Lam, William Fu, Amin Fazel, Jeremy Holleman, Xinyuan Huang, Robert Hurtado, David Kanter, Anton Lokhmotov, David A. Patterson, Danilo Pau, Jae-sun Seo, Jeff Sieracki, Urmish Thakker, Marian Verhelst, Poonam Yadav:
Benchmarking TinyML Systems: Challenges and Direction. CoRR abs/2003.04821 (2020)
2010 – 2019
- 2019
- [j2]Jeffrey Regier
Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. 127: 89-104 (2019) - [i6]Srivatsan Krishnan, Sharad Chitlangia
Quantized Reinforcement Learning (QUARL). CoRR abs/1910.01055 (2019) - 2018
- [j1]Kangwook Lee
Speeding Up Distributed Machine Learning Using Codes. IEEE Trans. Inf. Theory 64(3): 1514-1529 (2018) - [c4]Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. AISTATS 2018: 1998-2007 - [c3]Jeffrey Regier
Cataloging the Visible Universe Through Bayesian Inference at Petascale. IPDPS 2018: 44-53 - [c2]Jian Zhang, Max Lam, Stephanie Wang, Paroma Varma, Luigi Nardi, Kunle Olukotun
Exploring the Utility of Developer Exhaust. DEEM@SIGMOD 2018: 7:1-7:7 - [i5]Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat:
Cataloging the Visible Universe through Bayesian Inference at Petascale. CoRR abs/1801.10277 (2018) - [i4]Maximilian Lam:
Word2Bits - Quantized Word Vectors. CoRR abs/1803.05651 (2018) - 2017
- [i3]Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett:
Gradient Diversity Empowers Distributed Learning. CoRR abs/1706.05699 (2017) - 2016
- [c1]Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran:
Speeding up distributed machine learning using codes. ISIT 2016: 1143-1147 - [i2]Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht:
CYCLADES: Conflict-free Asynchronous Machine Learning. CoRR abs/1605.09721 (2016) - 2015
- [i1]Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran:
Speeding Up Distributed Machine Learning Using Codes. CoRR abs/1512.02673 (2015)
Coauthor Index
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