| On the opportunities and risks of foundation models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4268 | 2021 |
| Learning imbalanced datasets with label-distribution-aware margin loss K Cao, C Wei, A Gaidon, N Arechiga, T Ma NeurIPS 2019; arXiv preprint arXiv:1906.07413, 2019 | 1804 | 2019 |
| A simple but tough-to-beat baseline for sentence embeddings S Arora, Y Liang, T Ma ICLR 2017, 2016 | 1727 | 2016 |
| Mopo: Model-based offline policy optimization T Yu, G Thomas, L Yu, S Ermon, JY Zou, S Levine, C Finn, T Ma Advances in Neural Information Processing Systems 33, 14129-14142, 2020 | 862 | 2020 |
| Generalization and Equilibrium in Generative Adversarial Nets (GANs) S Arora, R Ge, Y Liang, T Ma, Y Zhang ICML 2017;arXiv preprint arXiv:1703.00573, 2017, 2017 | 836 | 2017 |
| Matrix Completion has No Spurious Local Minimum R Ge, JD Lee, T Ma NIPS 2016 (best student paper). arXiv preprint arXiv:1605.07272, 2016 | 730 | 2016 |
| A latent variable model approach to pmi-based word embeddings S Arora, Y Li, Y Liang, T Ma, A Risteski Transactions of the Association for Computational Linguistics 4, 385-399, 2016 | 674 | 2016 |
| Fine-tuning can distort pretrained features and underperform out-of-distribution A Kumar, A Raghunathan, R Jones, T Ma, P Liang arXiv preprint arXiv:2202.10054, 2022 | 673 | 2022 |
| An explanation of in-context learning as implicit bayesian inference SM Xie, A Raghunathan, P Liang, T Ma arXiv preprint arXiv:2111.02080, 2021 | 630 | 2021 |
| Provable bounds for learning some deep representations S Arora, A Bhaskara, R Ge, T Ma International conference on machine learning, 584-592, 2014 | 454 | 2014 |
| Identity Matters in Deep Learning M Hardt, T Ma ICLR 2017, 2016 | 446 | 2016 |
| What learning algorithm is in-context learning? investigations with linear models E Akyürek, D Schuurmans, J Andreas, T Ma, D Zhou arXiv preprint arXiv:2211.15661, 2022 | 424 | 2022 |
| Verified uncertainty calibration A Kumar, PS Liang, T Ma Advances in Neural Information Processing Systems 32, 2019 | 388 | 2019 |
| Fixup initialization: Residual learning without normalization H Zhang, YN Dauphin, T Ma arXiv preprint arXiv:1901.09321, 2019 | 386 | 2019 |
| Algorithmic Regularization in Over-parameterized Matrix Recovery and Neural Networks with Quadratic Activations Y Li, T Ma, H Zhang COLT 2018 (best paper); arXiv preprint arXiv:1712.09203, 2017 | 367* | 2017 |
| Finding Approximate Local Minima for Nonconvex Optimization in Linear Time N Agarwal, Z Allen-Zhu, B Bullins, E Hazan, T Ma STOC 2017, 2016 | 360* | 2016 |
| Towards explaining the regularization effect of initial large learning rate in training neural networks Y Li, C Wei, T Ma Advances in neural information processing systems 32, 2019 | 359 | 2019 |
| Gradient descent learns linear dynamical systems M Hardt, T Ma, B Recht Journal of Machine Learning Research 19 (29), 1-44, 2018 | 359 | 2018 |
| Learning one-hidden-layer neural networks with landscape design R Ge, JD Lee, T Ma ICLR 2017; arXiv preprint arXiv:1711.00501, 2017 | 309 | 2017 |
| Provable guarantees for self-supervised deep learning with spectral contrastive loss JZ HaoChen, C Wei, A Gaidon, T Ma Advances in Neural Information Processing Systems 34, 5000-5011, 2021 | 304 | 2021 |
Colin WeiStanford University在 stanford.edu 的电子邮件经过验证
