Maximilian Lam
Maximilian Lam
Verified email at g.harvard.edu - Homepage
Title
Cited by
Cited by
Year
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
5862017
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
85*2017
Cyclades: Conflict-free asynchronous machine learning
X Pan, M Lam, S Tu, D Papailiopoulos, C Zhang, MI Jordan, ...
arXiv preprint arXiv:1605.09721, 2016
572016
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
522020
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
29*2019
Word2bits-quantized word vectors
M Lam
arXiv preprint arXiv:1803.05651, 2018
202018
Quantized reinforcement learning (quarl)
M Lam, S Chitlangia, S Krishnan, Z Wan, G Barth-Maron, A Faust, ...
arXiv preprint arXiv:1910.01055, 2019
112019
Quantized neural network inference with precision batching
M Lam, Z Yedidia, C Banbury, VJ Reddi
arXiv preprint arXiv:2003.00822, 2020
22020
Gradient Disaggregation: Breaking Privacy in Federated Learning by Reconstructing the User Participant Matrix
M Lam, GY Wei, D Brooks, VJ Reddi, M Mitzenmacher
arXiv preprint arXiv:2106.06089, 2021
12021
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
12021
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
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
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
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, ...
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