| Convergence diagnostics for stochastic gradient descent with constant learning rate J Chee, P Toulis International Conference on Artificial Intelligence and Statistics, 1476-1485, 2018 | 55 | 2018 |
| Quip: 2-bit quantization of large language models with guarantees J Chee, Y Cai, V Kuleshov, CM De Sa Advances in Neural Information Processing Systems 36, 2024 | 29 | 2024 |
| Understanding and detecting convergence for stochastic gradient descent with momentum J Chee, P Li 2020 IEEE international conference on big data (Big Data), 133-140, 2020 | 14 | 2020 |
| Convergence diagnostics for stochastic gradient descent with constant step size J Chee, P Toulis arXiv preprint arXiv:1710.06382, 2017 | 11 | 2017 |
| Model preserving compression for neural networks J Chee, A Damle, CM De Sa Advances in Neural Information Processing Systems 35, 38060-38074, 2022 | 5 | 2022 |
| “Plus/minus the learning rate”: Easy and Scalable Statistical Inference with SGD J Chee, H Kim, P Toulis International Conference on Artificial Intelligence and Statistics, 2285-2309, 2023 | 4 | 2023 |
| Quip#: Even better LLM quantization with hadamard incoherence and lattice codebooks A Tseng, J Chee, Q Sun, V Kuleshov, C De Sa arXiv preprint arXiv:2402.04396, 2024 | 3 | 2024 |
| How low can we go: Trading memory for error in low-precision training C Yang, Z Wu, J Chee, C De Sa, M Udell arXiv preprint arXiv:2106.09686, 2021 | 3 | 2021 |
| Pruning neural networks with interpolative decompositions J Chee, M Renz, A Damle, C De Sa arXiv preprint arXiv:2108.00065, 2021 | 2 | 2021 |
| Performance optimizations on U-Net speech enhancement models J Chee, S Braun, V Gopal, R Cutler 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP …, 2022 | | 2022 |
| Performance optimizations on deep noise suppression models J Chee, S Braun, V Gopal, R Cutler arXiv preprint arXiv:2110.04378, 2021 | | 2021 |
Christopher De SaAssistant Professor of Computer Science, Cornell UniversityVerified email at cs.cornell.edu
