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Maximilian Lam
Maximilian Lam
g.harvard.edu의 이메일 확인됨 - 홈페이지
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Speeding up distributed machine learning using codes
K Lee, M Lam, R Pedarsani, D Papailiopoulos, K Ramchandran
IEEE Transactions on Information Theory 64 (3), 1514-1529, 2017
7012017
Benchmarking TinyML systems: Challenges and direction
CR Banbury, VJ Reddi, M Lam, W Fu, A Fazel, J Holleman, X Huang, ...
arXiv preprint arXiv:2003.04821, 2020
1272020
Gradient diversity: a key ingredient for scalable distributed learning
D Yin, A Pananjady, M Lam, D Papailiopoulos, K Ramchandran, P Bartlett
Proceedings of the 21th International Conference on Artificial Intelligence …, 2017
109*2017
Cyclades: Conflict-free asynchronous machine learning
X Pan, M Lam, S Tu, D Papailiopoulos, C Zhang, MI Jordan, ...
Advances in Neural Information Processing Systems 29, 2016
622016
Cataloging the visible universe through Bayesian inference in Julia at petascale
J Regier, K Fischer, K Pamnany, A Noack, J Revels, M Lam, S Howard, ...
Journal of Parallel and Distributed Computing 127, 89-104, 2019
35*2019
Word2bits-quantized word vectors
M Lam
arXiv preprint arXiv:1803.05651, 2018
232018
Quantized reinforcement learning (quarl)
S Krishnan, S Chitlangia, M Lam, Z Wan, A Faust, VJ Reddi
122019
Gradient disaggregation: Breaking privacy in federated learning by reconstructing the user participant matrix
M Lam, GY Wei, D Brooks, VJ Reddi, M Mitzenmacher
International Conference on Machine Learning, 5959-5968, 2021
112021
The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage
D Galvez, G Diamos, J Ciro, JF Cerón, K Achorn, A Gopi, D Kanter, M Lam, ...
arXiv preprint arXiv:2111.09344, 2021
102021
Widening access to applied machine learning with tinyML
VJ Reddi, B Plancher, S Kennedy, L Moroney, P Warden, A Agarwal, ...
arXiv preprint arXiv:2106.04008, 2021
102021
Widening access to applied machine learning with tinyml
V Janapa Reddi, B Plancher, S Kennedy, L Moroney, P Warden, ...
arXiv e-prints, arXiv: 2106.04008, 2021
32021
Quantized reinforcement learning (quarl)
M Lam, S Chitlangia, S Krishnan, Z Wan, G Barth-Maron, A Faust, ...
arXiv preprint arXiv:1910.01055, 2019
32019
Quantized neural network inference with precision batching
M Lam, Z Yedidia, C Banbury, VJ Reddi
arXiv preprint arXiv:2003.00822, 2020
22020
QuaRL: Quantization for sustainable reinforcement learning
S Krishnan, M Lam, S Chitlangia, Z Wan, G Barth-Maron, A Faust, ...
arXiv e-prints, arXiv: 1910.01055, 2019
12019
Exploring the Utility of Developer Exhaust
J Zhang, M Lam, S Wang, P Varma, L Nardi, K Olukotun, C Ré
Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018
12018
Tabula: Efficiently Computing Nonlinear Activation Functions for Secure Neural Network Inference
M Lam, M Mitzenmacher, VJ Reddi, GY Wei, D Brooks
arXiv preprint arXiv:2203.02833, 2022
2022
Precision Batching: Bitserial Decomposition for Efficient Neural Network Inference on GPUs
M Lam, Z Yedidia, CR Banbury, VJ Reddi
2021 30th International Conference on Parallel Architectures and Compilation …, 2021
2021
QUARL: QUANTIZED REINFORCEMENT LEARNING
S Krishnan, S Chitlangia, M Lam, Z Wan, A Faust, VJ Reddi
2020
2021 30th International Conference on Parallel Architectures and Compilation Techniques (PACT)| 978-1-6654-4278-7/21/$31.00© 2021 IEEE| DOI: 10.1109/PACT52795. 2021.00033
B Akin, C Angermueller, D Baek, W Baek, CR Banbury, Y Bao, A Basu, ...
ACTORQ: QUANTIZATION FOR ACTOR-LEARNER DISTRIBUTED REINFORCEMENT LEARNING
M Lam, S Chitlangia, S Krishnan, Z Wan, G Barth-Maron, A Faust, ...
update 400, 600, 0
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학술자료 1–20