| Deep neural networks as gaussian processes J Lee, Y Bahri, R Novak, SS Schoenholz, J Pennington, J Sohl-Dickstein arXiv preprint arXiv:1711.00165, 2017 | 359 | 2017 |
| Wide neural networks of any depth evolve as linear models under gradient descent J Lee, L Xiao, SS Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ... arXiv preprint arXiv:1902.06720, 2019 | 256 | 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 | 190 | 2015 |
| Sensitivity and generalization in neural networks: an empirical study R Novak, Y Bahri, DA Abolafia, J Pennington, J Sohl-Dickstein arXiv preprint arXiv:1802.08760, 2018 | 187 | 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 https://arxiv.org/abs/1806.05393, 2018 | 140 | 2018 |
| Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes R Novak, L Xiao, J Lee, Y Bahri, D Abolafia, J Pennington, ... https://arxiv.org/abs/1810.05148, 2018 | 105* | 2018 |
| Geometry of neural network loss surfaces via random matrix theory J Pennington, Y Bahri International Conference on Machine Learning, 2798-2806, 2017 | 82 | 2017 |
| Phonon analog of topological nodal semimetals HC Po, Y Bahri, A Vishwanath Physical Review B 93 (20), 205158, 2016 | 40 | 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 | 32 | 2009 |
| Statistical mechanics of deep learning Y Bahri, J Kadmon, J Pennington, SS Schoenholz, J Sohl-Dickstein, ... Annual Review of Condensed Matter Physics, 2020 | 30 | 2020 |
| 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 | 22 | 2020 |
| Detecting Majorana fermions in quasi-one-dimensional topological phases using nonlocal order parameters Y Bahri, A Vishwanath Physical review b 89 (15), 155135, 2014 | 19 | 2014 |
| Stable non-Fermi-liquid phase of itinerant spin-orbit coupled ferromagnets Y Bahri, AC Potter Physical Review B 92 (3), 035131, 2015 | 6 | 2015 |
| 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 | 5 | 2020 |
| Bayesian deep convolutional neural networks with many channels are gaussian processes R Novak, L Xiao, J Lee, Y Bahri, G Yang, D Abolafia, J Pennington, ... | 2 | 2019 |
| Wide neural networks of any depth evolve as linear models under gradient descent J Lee, L Xiao, SS Schoenholz, Y Bahri, R Novak, J Sohl-Dickstein, ... Journal of Statistical Mechanics: Theory and Experiment 2020 (12), 124002, 2020 | 1 | 2020 |
| 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 | 1 | 2020 |
| Quantum Phenomena in Interacting Many-Body Systems: Topological Protection, Localization, and Non-Fermi Liquids Y Bahri UC Berkeley, 2017 | | 2017 |
| Exact posterior distributions of wide Bayesian neural networks Download PDF J Hron, Y Bahri, R Novak, J Pennington, J Sohl-Dickstein | | |
| Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes Download PDF R Novak, L Xiao, Y Bahri, J Lee, G Yang, J Hron, DA Abolafia, ... | | |
