Maximilian Lam

According to our database, Maximilian Lam authored at least 21 papers between 2016 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

GPU-based Private Information Retrieval for On-Device Machine Learning Inference.

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

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, 2024

2023

GWAS quality score for evaluating associated regions in GWAS analyses.

Swapnil Awasthi, Chia-Yen Chen, Max Lam, Hailiang Huang, Stephan Ripke, C. Anthony Altar

Bioinform., January, 2023

2022

QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement Learning.

Srivatsan Krishnan, Max Lam, Sharad Chitlangia, Zishen Wan, Gabriel Barth-Maron, Aleksandra Faust, Vijay Janapa Reddi

Trans. Mach. Learn. Res., 2022

Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference.

Maximilian Lam, Michael Mitzenmacher, Vijay Janapa Reddi, Gu-Yeon Wei, David Brooks

CoRR, 2022

2021

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage.

Daniel Galvez, Greg Diamos, Juan Ciro, Juan Felipe Cerón, Keith Achorn, Anjali Gopi, David Kanter, Maximilian Lam, Mark Mazumder, Vijay Janapa Reddi

CoRR, 2021

Widening Access to Applied Machine Learning with TinyML.

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

CoRR, 2021

The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage.

Daniel Galvez, Greg Diamos, Juan Torres, Keith Achorn, Juan Felipe Cerón, Anjali Gopi, David Kanter, Max Lam, Mark Mazumder, Vijay Janapa Reddi

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix.

Maximilian Lam, Gu-Yeon Wei, David Brooks, Vijay Janapa Reddi, Michael Mitzenmacher

Proceedings of the 38th International Conference on Machine Learning, 2021

Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs.

Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi

Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques, 2021

2020

Benchmarking TinyML Systems: Challenges and Direction.

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

CoRR, 2020

Quantized Neural Network Inference with Precision Batching.

Maximilian Lam, Zachary Yedidia, Colby R. Banbury, Vijay Janapa Reddi

CoRR, 2020

RICOPILI: Rapid Imputation for COnsortias PIpeLIne.

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

Bioinform., 2020

2019

Cataloging the visible universe through Bayesian inference in Julia at petascale.

Jeffrey Regier, Keno Fischer, Kiran Pamnany, Andreas Noack, Jarrett Revels, Maximilian Lam, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat

J. Parallel Distributed Comput., 2019

Quantized Reinforcement Learning (QUARL).

Srivatsan Krishnan, Sharad Chitlangia, Maximilian Lam, Zishen Wan, Aleksandra Faust, Vijay Janapa Reddi

CoRR, 2019

2018

Speeding Up Distributed Machine Learning Using Codes.

Kangwook Lee, Maximilian Lam, Ramtin Pedarsani, Dimitris S. Papailiopoulos, Kannan Ramchandran

IEEE Trans. Inf. Theory, 2018

Word2Bits - Quantized Word Vectors.

Maximilian Lam

CoRR, 2018

Exploring the Utility of Developer Exhaust.

Jian Zhang, Max Lam, Stephanie Wang, Paroma Varma, Luigi Nardi, Kunle Olukotun, Christopher Ré

Proceedings of the Second Workshop on Data Management for End-To-End Machine Learning, 2018

Cataloging the Visible Universe Through Bayesian Inference at Petascale.

Jeffrey Regier, Kiran Pamnany, Keno Fischer, Andreas Noack, Maximilian Lam, Jarrett Revels, Steve Howard, Ryan Giordano, David Schlegel, Jon McAuliffe, Rollin C. Thomas, Prabhat

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium, 2018

Gradient Diversity: a Key Ingredient for Scalable Distributed Learning.

Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Gradient Diversity Empowers Distributed Learning.

Dong Yin, Ashwin Pananjady, Maximilian Lam, Dimitris S. Papailiopoulos, Kannan Ramchandran, Peter L. Bartlett

CoRR, 2017

2016

CYCLADES: Conflict-free Asynchronous Machine Learning.

Xinghao Pan, Maximilian Lam, Stephen Tu, Dimitris S. Papailiopoulos, Ce Zhang, Michael I. Jordan, Kannan Ramchandran, Christopher Ré, Benjamin Recht

CoRR, 2016