| Spectrum dependent learning curves in kernel regression and wide neural networks B Bordelon, A Canatar, C Pehlevan International Conference on Machine Learning, 1024-1034, 2020 | 204 | 2020 |
| Spectral bias and task-model alignment explain generalization in kernel regression and infinitely wide neural networks A Canatar, B Bordelon, C Pehlevan Nature communications 12 (1), 2914, 2021 | 189 | 2021 |
| Neural networks as kernel learners: The silent alignment effect A Atanasov, B Bordelon, C Pehlevan arXiv preprint arXiv:2111.00034, 2021 | 78 | 2021 |
| Self-consistent dynamical field theory of kernel evolution in wide neural networks B Bordelon, C Pehlevan Advances in Neural Information Processing Systems 35, 32240-32256, 2022 | 69 | 2022 |
| Dispersive optical model analysis of generating a neutron-skin prediction beyond the mean field MC Atkinson, MH Mahzoon, MA Keim, BA Bordelon, CD Pruitt, RJ Charity, ... Physical Review C 101 (4), 044303, 2020 | 41 | 2020 |
| Dynamics of finite width kernel and prediction fluctuations in mean field neural networks B Bordelon, C Pehlevan Advances in Neural Information Processing Systems 36, 2024 | 27 | 2024 |
| Grokking as the transition from lazy to rich training dynamics T Kumar, B Bordelon, SJ Gershman, C Pehlevan arXiv preprint arXiv:2310.06110, 2023 | 26 | 2023 |
| The onset of variance-limited behavior for networks in the lazy and rich regimes A Atanasov, B Bordelon, S Sainathan, C Pehlevan arXiv preprint arXiv:2212.12147, 2022 | 24 | 2022 |
| The influence of learning rule on representation dynamics in wide neural networks B Bordelon, C Pehlevan The Eleventh International Conference on Learning Representations, 2022 | 24 | 2022 |
| Learning Curves for SGD on Structured Features B Bordelon, C Pehlevan International Conference on Learning Representations, 2022 | 24 | 2022 |
| A dynamical model of neural scaling laws B Bordelon, A Atanasov, C Pehlevan arXiv preprint arXiv:2402.01092, 2024 | 22 | 2024 |
| Feature-learning networks are consistent across widths at realistic scales N Vyas, A Atanasov, B Bordelon, D Morwani, S Sainathan, C Pehlevan Advances in Neural Information Processing Systems 36, 2024 | 21 | 2024 |
| Out-of-distribution generalization in kernel regression A Canatar, B Bordelon, C Pehlevan Advances in Neural Information Processing Systems 34, 12600-12612, 2021 | 20 | 2021 |
| Population codes enable learning from few examples by shaping inductive bias B Bordelon, C Pehlevan Elife 11, e78606, 2022 | 18 | 2022 |
| A theory of neural tangent kernel alignment and its influence on training H Shan, B Bordelon arXiv preprint arXiv:2105.14301, 2021 | 18* | 2021 |
| Depthwise hyperparameter transfer in residual networks: Dynamics and scaling limit B Bordelon, L Noci, MB Li, B Hanin, C Pehlevan arXiv preprint arXiv:2309.16620, 2023 | 16 | 2023 |
| Infinite Limits of Multi-head Transformer Dynamics B Bordelon, HT Chaudhry, C Pehlevan arXiv preprint arXiv:2405.15712, 2024 | 5 | 2024 |
| Loss dynamics of temporal difference reinforcement learning B Bordelon, P Masset, H Kuo, C Pehlevan Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Self-consistent dynamical field theory of kernel evolution in wide neural networks B Bordelon, C Pehlevan Journal of Statistical Mechanics: Theory and Experiment 2023 (11), 114009, 2023 | 5 | 2023 |
| Efficient online inference for nonparametric mixture models R Schaeffer, B Bordelon, M Khona, W Pan, IR Fiete Uncertainty in Artificial Intelligence, 2072-2081, 2021 | 4 | 2021 |
Cengiz PehlevanHarvard UniversityVerified email at seas.harvard.edu