| 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 | 701 | 2017 |
| 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 | 127 | 2020 |
| 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 | 62 | 2016 |
| 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 | 23 | 2018 |
| Quantized reinforcement learning (quarl) S Krishnan, S Chitlangia, M Lam, Z Wan, A Faust, VJ Reddi | 12 | 2019 |
| 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 | 11 | 2021 |
| 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 | 10 | 2021 |
| 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 | 10 | 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 | 3 | 2021 |
| Quantized reinforcement learning (quarl) M Lam, S Chitlangia, S Krishnan, Z Wan, G Barth-Maron, A Faust, ... arXiv preprint arXiv:1910.01055, 2019 | 3 | 2019 |
| Quantized neural network inference with precision batching M Lam, Z Yedidia, C Banbury, VJ Reddi arXiv preprint arXiv:2003.00822, 2020 | 2 | 2020 |
| 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 | 1 | 2019 |
| 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 | 1 | 2018 |
| 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 | | |
