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Maximilian Lam
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2020 – today
- 2024
- [c8]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. ASPLOS (1) 2024: 197-214 - 2023
- [j5]Swapnil Awasthi
, Chia-Yen Chen, Max Lam
, Hailiang Huang, Stephan Ripke
, C. Anthony Altar:
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
, Laurence Moroney, Pete Warden, Anant Agarwal, Colby R. Banbury, Massimo Banzi, Matthew Bennett, Benjamin Brown, Sharad Chitlangia, Radhika Ghosal, Sarah Grafman, Rupert Jaeger, Srivatsan Krishnan, Maximilian Lam, Daniel Leiker, Cara Mann, Mark Mazumder, Dominic Pajak, Dhilan Ramaprasad, J. Evan Smith, Matthew Stewart
, Dustin Tingley:
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
, Swapnil Awasthi
, Hunna J. Watson, Jackie Goldstein, Georgia Panagiotaropoulou
, Vassily Trubetskoy
, Robert Karlsson
, Oleksander Frei, Chun-Chieh Fan, Ward De Witte, Nina R. Mota
, Niamh Mullins, Kim Brügger
, Sang Hong Lee
, Naomi R. Wray
, Nora Skarabis, Hailiang Huang, Benjamin M. Neale, Mark J. Daly, Manuel Mattheisen, Raymond Walters, Stephan Ripke
:
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
, Keno Fischer
, Kiran Pamnany, Andreas Noack
, Jarrett Revels, Maximilian Lam, Steve Howard
, Ryan Giordano, David Schlegel
, Jon McAuliffe
, Rollin C. Thomas, Prabhat
:
Cataloging the visible universe through Bayesian inference in Julia at petascale. J. Parallel Distributed Comput. 127: 89-104 (2019) - [i6]Srivatsan Krishnan, Sharad Chitlangia
, Maximilian Lam, Zishen Wan
, Aleksandra Faust, Vijay Janapa Reddi:
Quantized Reinforcement Learning (QUARL). CoRR abs/1910.01055 (2019) - 2018
- [j1]Kangwook Lee
, Maximilian Lam, Ramtin Pedarsani
, Dimitris S. Papailiopoulos, Kannan Ramchandran:
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
, 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. IPDPS 2018: 44-53 - [c2]Jian Zhang, Max Lam, Stephanie Wang, Paroma Varma, Luigi Nardi, Kunle Olukotun
, Christopher Ré:
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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last updated on 2024-06-16 23:53 CEST by the dblp team
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