| Deep neural networks as gaussian processes J Lee*, Y Bahri*, R Novak, SS Schoenholz, J Pennington, ... International Conference on Learning Representations, 2018, 2018 | 909 | 2018 |
| Wide neural networks of any depth evolve as linear models under gradient descent J Lee*, L Xiao*, S Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ... Advances in neural information processing systems 32, 2019 | 762 | 2019 |
| Sensitivity and generalization in neural networks: an empirical study R Novak, Y Bahri, DA Abolafia, J Pennington, J Sohl-Dickstein International Conference on Learning Representations, 2018, 2018 | 390 | 2018 |
| Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks L Xiao, Y Bahri, J Sohl-Dickstein, SS Schoenholz, J Pennington International Conference on Machine Learning, 2018, 2018 | 287 | 2018 |
| Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes R Novak^, L Xiao^, J Lee*, Y Bahri*, D Abolafia, J Pennington, ... International Conference on Learning Representations, 2019, 2019 | 272* | 2019 |
| Localization and topology protected quantum coherence at the edge of hot matter Y Bahri, R Vosk, E Altman, A Vishwanath Nature communications 6, 7341, 2015 | 247 | 2015 |
| Statistical mechanics of deep learning Y Bahri, J Kadmon, J Pennington, SS Schoenholz, J Sohl-Dickstein, ... Annual Review of Condensed Matter Physics 11, 501-528, 2020 | 176 | 2020 |
| Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models A Srivastava, et al. https://arxiv.org/abs/2206.04615, 2022 | 175 | 2022 |
| Geometry of neural network loss surfaces via random matrix theory J Pennington, Y Bahri International Conference on Machine Learning, 2798-2806, 2017 | 141 | 2017 |
| The large learning rate phase of deep learning: the catapult mechanism A Lewkowycz, Y Bahri, E Dyer, J Sohl-Dickstein, G Gur-Ari arXiv preprint arXiv:2003.02218, 2020 | 130 | 2020 |
| Explaining neural scaling laws Y Bahri, E Dyer, J Kaplan, J Lee, U Sharma arXiv preprint arXiv:2102.06701, 2021 | 75 | 2021 |
| Infinite attention: NNGP and NTK for deep attention networks J Hron, Y Bahri, J Sohl-Dickstein, R Novak International Conference on Machine Learning, 4376-4386, 2020 | 69 | 2020 |
| Phonon analog of topological nodal semimetals HC Po, Y Bahri, A Vishwanath Physical Review B 93 (20), 205158, 2016 | 53 | 2016 |
| Spatial resolution of a type II heterojunction in a single bipolar molecule C Tao, J Sun, X Zhang, R Yamachika, D Wegner, Y Bahri, G Samsonidze, ... Nano letters 9 (12), 3963-3967, 2009 | 34 | 2009 |
| The evolution of out-of-distribution robustness throughout fine-tuning A Andreassen, Y Bahri, B Neyshabur, R Roelofs Transactions of Machine Learning Research, 2021 | 29 | 2021 |
| Detecting Majorana fermions in quasi-one-dimensional topological phases using nonlocal order parameters Y Bahri, A Vishwanath Physical review b 89 (15), 155135, 2014 | 28 | 2014 |
| Exact posterior distributions of wide Bayesian neural networks J Hron, Y Bahri, R Novak, J Pennington, J Sohl-Dickstein arXiv preprint arXiv:2006.10541, 2020 | 20 | 2020 |
| Stable non-Fermi-liquid phase of itinerant spin-orbit coupled ferromagnets Y Bahri, AC Potter Physical Review B 92 (3), 035131, 2015 | 9 | 2015 |
| Scaling laws for deep neural networks Y Bahri AI and Optical Data Sciences IV, PC124380J, 2023 | | 2023 |
| Scaling Laws in Deep Neural Networks: Insights from Statistical Mechanics and Exactly Solvable Models Y Bahri Bulletin of the American Physical Society, 2023 | | 2023 |
