| Exploring generalization in deep learning B Neyshabur, S Bhojanapalli, D McAllester, N Srebro Advances in Neural Information Processing Systems, 2017 | 1342 | 2017 |
| Sharpness-Aware Minimization for Efficiently Improving Generalization P Foret, A Kleiner, H Mobahi, B Neyshabur International Conference on Learning Representations, 2021 | 1141 | 2021 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 843 | 2023 |
| Beyond the imitation game: Quantifying and extrapolating the capabilities of language models A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ... Transactions on Machine Learning Research, 2023 | 828 | 2023 |
| In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning B Neyshabur, R Tomioka, N Srebro International Conference on Learning Representations, 2015 | 686 | 2015 |
| Stronger generalization bounds for deep nets via a compression approach S Arora, R Ge, B Neyshabur, Y Zhang The 35th International Conference on Machine Learning, 2018 | 670 | 2018 |
| A pac-bayesian approach to spectrally-normalized margin bounds for neural networks B Neyshabur, S Bhojanapalli, N Srebro International Conference on Learning Representations, 2018 | 634 | 2018 |
| Norm-Based Capacity Control in Neural Networks B Neyshabur, R Tomioka, N Srebro Conference on Learning Theory, 1376–1401, 2015 | 612 | 2015 |
| Towards understanding the role of over-parametrization in generalization of neural networks B Neyshabur, Z Li, S Bhojanapalli, Y LeCun, N Srebro International Conference on Learning Representations, 2019 | 582 | 2019 |
| Fantastic Generalization Measures and Where to Find Them Y Jiang, B Neyshabur, H Mobahi, D Krishnan, S Bengio International Conference on Learning Representations, 2020 | 571 | 2020 |
| Implicit regularization in matrix factorization S Gunasekar, BE Woodworth, S Bhojanapalli, B Neyshabur, N Srebro Advances in neural information processing systems 30, 2017 | 511 | 2017 |
| Solving quantitative reasoning problems with language models A Lewkowycz, A Andreassen, D Dohan, E Dyer, H Michalewski, ... Advances in Neural Information Processing Systems, 2022 | 447 | 2022 |
| What is being transferred in transfer learning? B Neyshabur, H Sedghi, C Zhang Advances in Neural Information Processing Systems, 2020 | 443 | 2020 |
| Global Optimality of Local Search for Low Rank Matrix Recovery S Bhojanapalli, B Neyshabur, N Srebro Advances in Neural Information Processing Systems, 2016 | 433 | 2016 |
| Path-SGD: Path-Normalized Optimization in Deep Neural Networks B Neyshabur, RR Salakhutdinov, N Srebro Advances in Neural Information Processing Systems, 2413-2421, 2015 | 324 | 2015 |
| Predicting protein–protein interactions through sequence-based deep learning S Hashemifar, B Neyshabur, AA Khan, J Xu Bioinformatics 34 (17), i802-i810, 2018 | 322 | 2018 |
| On Symmetric and Asymmetric LSHs for Inner Product Search B Neyshabur, N Srebro The 32nd International Conference on Machine Learning, 1926–1934, 2015 | 215 | 2015 |
| NETAL: a new graph-based method for global alignment of protein–protein interaction networks B Neyshabur, A Khadem, S Hashemifar, SS Arab Bioinformatics 29 (13), 1654-1662, 2013 | 208 | 2013 |
| Implicit regularization in deep learning B Neyshabur PhD Thesis, 2017 | 178 | 2017 |
| Corralling a band of bandit algorithms A Agarwal, H Luo, B Neyshabur, RE Schapire Conference on Learning Theory, 2017 | 170 | 2017 |
Nathan SrebroProfessor, TTIC and University of Chicago在 ttic.edu 的电子邮件经过验证
