| An introduction to computational networks and the computational network toolkit D Yu, A Eversole, M Seltzer, K Yao, Z Huang, B Guenter, O Kuchaiev, ... Microsoft Technical Report MSR-TR-2014–112, 2014 | 473 | 2014 |
| Contextual embeddings: When are they worth it? S Arora, A May, J Zhang, C Ré arXiv preprint arXiv:2005.09117, 2020 | 93 | 2020 |
| How to scale up kernel methods to be as good as deep neural nets Z Lu, A May, K Liu, AB Garakani, D Guo, A Bellet, L Fan, M Collins, ... arXiv preprint arXiv:1411.4000, 2014 | 69 | 2014 |
| Low-precision random Fourier features for memory-constrained kernel approximation J Zhang, A May, T Dao, C Ré The 22nd International Conference on Artificial Intelligence and Statistics …, 2019 | 40 | 2019 |
| Sequoia: Scalable, robust, and hardware-aware speculative decoding Z Chen, A May, R Svirschevski, Y Huang, M Ryabinin, Z Jia, B Chen arXiv preprint arXiv:2402.12374, 2024 | 28 | 2024 |
| On the downstream performance of compressed word embeddings A May, J Zhang, T Dao, C Ré Advances in neural information processing systems 32, 2019 | 28 | 2019 |
| Filter & follow: How social media foster content curation A May, A Chaintreau, N Korula, S Lattanzi The 2014 ACM international conference on Measurement and modeling of …, 2014 | 28 | 2014 |
| The mamba in the llama: Distilling and accelerating hybrid models J Wang, D Paliotta, A May, A Rush, T Dao Advances in Neural Information Processing Systems 37, 62432-62457, 2024 | 23 | 2024 |
| Kernel approximation methods for speech recognition A May Columbia University, 2018 | 22 | 2018 |
| Understanding the downstream instability of word embeddings M Leszczynski, A May, J Zhang, S Wu, C Aberger, C Ré Proceedings of Machine Learning and Systems 2, 262-290, 2020 | 18 | 2020 |
| Kernel approximation methods for speech recognition A May, AB Garakani, Z Lu, D Guo, K Liu, A Bellet, L Fan, M Collins, D Hsu, ... Journal of Machine Learning Research 20 (59), 1-36, 2019 | 16 | 2019 |
| A comparison between deep neural nets and kernel acoustic models for speech recognition Z Lu, D Quo, AB Garakani, K Liu, A May, A Bellet, L Fan, M Collins, ... 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 16 | 2016 |
| Specexec: Massively parallel speculative decoding for interactive llm inference on consumer devices R Svirschevski, A May, Z Chen, B Chen, Z Jia, M Ryabinin Advances in Neural Information Processing Systems 37, 16342-16368, 2024 | 13 | 2024 |
| Fei Sha A May, AB Garakani, Z Lu, D Guo, K Liu, A Bellet, L Fan, M Collins, ... Kernel approximation methods for speech recognition. CoRR, abs/1701.03577, 2017 | 7 | 2017 |
| Compact kernel models for acoustic modeling via random feature selection A May, M Collins, D Hsu, B Kingsbury 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 6 | 2016 |
| Audio-visual fine-tuning of audio-only ASR models A May, D Serdyuk, AP Shah, O Braga, O Siohan arXiv preprint arXiv:2312.09369, 2023 | 5 | 2023 |
| Game in the newsroom: Greedy bloggers for picky audience A May, A Chaintreau, N Korula, S Lattanzi Proc. of the 20th International Conference Companion on World Wide Web, 16-20, 2013 | 4 | 2013 |
| Sequoia: Scalable and robust speculative decoding Z Chen, A May, R Svirschevski, YH Huang, M Ryabinin, Z Jia, B Chen Advances in Neural Information Processing Systems 37, 129531-129563, 2024 | 2 | 2024 |
| Minions: Cost-efficient Collaboration Between On-device and Cloud Language Models A Narayan, D Biderman, S Eyuboglu, A May, S Linderman, J Zou, C Re arXiv preprint arXiv:2502.15964, 2025 | | 2025 |
| Self-stabilized deep neural network JG Droppo, P Ghahremani, A May US Patent 10,885,438, 2021 | | 2021 |
Christopher RéComputer Science, Stanford UniversityE-mailová adresa ověřena na: cs.stanford.edu
