Yasaman Bahri
Yasaman Bahri
Research Scientist, Google DeepMind (formerly Brain)
Verified email at - Homepage
Cited by
Cited by
Deep neural networks as gaussian processes
J Lee*, Y Bahri*, R Novak, SS Schoenholz, J Pennington, ...
International Conference on Learning Representations, 2018, 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
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
A Srivastava, et al., 2022
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
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
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
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
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
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
Geometry of neural network loss surfaces via random matrix theory
J Pennington, Y Bahri
International conference on machine learning, 2798-2806, 2017
Explaining neural scaling laws
Y Bahri, E Dyer, J Kaplan, J Lee, U Sharma
arXiv preprint arXiv:2102.06701, 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
The evolution of out-of-distribution robustness throughout fine-tuning
A Andreassen, Y Bahri, B Neyshabur, R Roelofs
Transactions of Machine Learning Research, 2021
Phonon analog of topological nodal semimetals
HC Po, Y Bahri, A Vishwanath
Physical Review B 93 (20), 205158, 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
Detecting Majorana fermions in quasi-one-dimensional topological phases using nonlocal order parameters
Y Bahri, A Vishwanath
Physical review b 89 (15), 155135, 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
Stable non-Fermi-liquid phase of itinerant spin-orbit coupled ferromagnets
Y Bahri, AC Potter
Physical Review B 92 (3), 035131, 2015
Quantum Many-Body Physics Calculations with Large Language Models
H Pan, N Mudur, W Taranto, M Tikhanovskaya, S Venugopalan, Y Bahri, ...
arXiv preprint arXiv:2403.03154, 2024
Performing Hartree-Fock many-body physics calculations with large language models
EA Kim, H Pan, N Mudur, W Taranto, S Venugopalan, Y Bahri, M Brenner
Bulletin of the American Physical Society, 2024
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