| Efficient streaming language models with attention sinks G Xiao, Y Tian, B Chen, S Han, M Lewis arXiv preprint arXiv:2309.17453, 2023 | 454 | 2023 |
| FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU Y Sheng, L Zheng, B Yuan, Z Li, M Ryabinin, B Chen, P Liang, C Re, ... International Conference on Machine Learning, 2023 | 357 | 2023 |
| H O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models Z Zhang, Y Sheng, T Zhou, T Chen, L Zheng, R Cai, Z Song, Y Tian, C Ré, ... International Conference on Machine Learning, 2023 | 313 | 2023 |
| Deja vu: Contextual sparsity for efficient llms at inference time Z Liu, J Wang, T Dao, T Zhou, B Yuan, Z Song, A Shrivastava, C Zhang, ... International Conference on Machine Learning, 22137-22176, 2023 | 265 | 2023 |
| Scatterbrain: Unifying sparse and low-rank attention B Chen, T Dao, E Winsor, Z Song, A Rudra, C Ré Advances in Neural Information Processing Systems 34, 17413-17426, 2021 | 142 | 2021 |
| SLIDE: In Defense of Smart Algorithms over Hardware Acceleration for Large-scale Deep Learning Systems B Chen, T Medini, J Farwell, S Gobriel, C Tai, A Shrivastava Proceedings of Machine Learning and System 2, 291--306, 2020 | 141 | 2020 |
| Galore: Memory-efficient llm training by gradient low-rank projection J Zhao, Z Zhang, B Chen, Z Wang, A Anandkumar, Y Tian arXiv preprint arXiv:2403.03507, 2024 | 132 | 2024 |
| KIVI: Plug-and-play 2bit KV Cache Quantization with Streaming Asymmetric Quantization Z Liu, J Yuan, H Jin, S Zhong, Z Xu, V Braverman, B Chen, X Hu | 112* | 2023 |
| Monarch: Expressive structured matrices for efficient and accurate training T Dao, B Chen, NS Sohoni, A Desai, M Poli, J Grogan, A Liu, A Rao, ... International Conference on Machine Learning, 4690-4721, 2022 | 103 | 2022 |
| Decentralized training of foundation models in heterogeneous environments B Yuan, Y He, JQ Davis, T Zhang, T Dao, B Chen, P Liang, C Re, C Zhang Neural Information Processing Systems., 2022 | 90 | 2022 |
| Pixelated butterfly: Simple and efficient sparse training for neural network models B Chen, T Dao, K Liang, J Yang, Z Song, A Rudra, C Re International Conference on Learning Representations, 2022 | 84* | 2022 |
| Scan and Snap: Understanding Training Dynamics and Token Composition in 1-layer Transformer Y Tian, Y Wang, B Chen, S Du International Conference on Machine Learning, 2023 | 80 | 2023 |
| MONGOOSE: A learnable LSH framework for efficient neural network training B Chen, Z Liu, B Peng, Z Xu, JL Li, T Dao, Z Song, A Shrivastava, C Re International Conference on Learning Representations, 2021 | 78 | 2021 |
| Analyzing log analysis: An empirical study of user log mining S Alspaugh, B Chen, J Lin, A Ganapathi, M Hearst, R Katz 28th Large Installation System Administration Conference (LISA14), 62-77, 2014 | 72 | 2014 |
| Llm inference unveiled: Survey and roofline model insights Z Yuan, Y Shang, Y Zhou, Z Dong, Z Zhou, C Xue, B Wu, Z Li, Q Gu, ... arXiv preprint arXiv:2402.16363, 2024 | 63 | 2024 |
| LayerSkip: Enabling early exit inference and self-speculative decoding M Elhoushi, A Shrivastava, D Liskovich, B Hosmer, B Wasti, L Lai, ... arXiv preprint arXiv:2404.16710, 2024 | 56 | 2024 |
| Fast and accurate stochastic gradient estimation B Chen, Y Xu, A Shrivastava Advances in Neural Information Processing Systems 32, 2019 | 53* | 2019 |
| Angular visual hardness B Chen, W Liu, Z Yu, J Kautz, A Shrivastava, A Garg, A Anandkumar International Conference on Machine Learning, 1637-1648, 2020 | 52 | 2020 |
| Joma: Demystifying multilayer transformers via joint dynamics of mlp and attention Y Tian, Y Wang, Z Zhang, B Chen, S Du arXiv preprint arXiv:2310.00535, 2023 | 51 | 2023 |
| Cocktailsgd: Fine-tuning foundation models over 500mbps networks J Wang, Y Lu, B Yuan, B Chen, P Liang, C De Sa, C Re, C Zhang International Conference on Machine Learning, 36058-36076, 2023 | 38 | 2023 |
Anshumali ShrivastavaRice University, ThirdAI Corp.Potvrđena adresa e-pošte na rice.edu