Yasaman Bahri

According to our database, Yasaman Bahri authored at least 15 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Quantum Many-Body Physics Calculations with Large Language Models.

Haining Pan, Nayantara Mudur, Will Taranto, Maria Tikhanovskaya, Subhashini Venugopalan, Yasaman Bahri, Michael P. Brenner, Eun-Ah Kim

CoRR, 2024

2023

Les Houches Lectures on Deep Learning at Large & Infinite Width.

Yasaman Bahri, Boris Hanin, Antonin Brossollet, Vittorio Erba, Christian Keup, Rosalba Pacelli, James B. Simon

CoRR, 2023

2022

The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning.

Anders Johan Andreassen, Yasaman Bahri, Behnam Neyshabur, Rebecca Roelofs

Trans. Mach. Learn. Res., 2022

2021

Explaining Neural Scaling Laws.

Yasaman Bahri, Ethan Dyer, Jared Kaplan, Jaehoon Lee, Utkarsh Sharma

CoRR, 2021

2020

Exact posterior distributions of wide Bayesian neural networks.

Jiri Hron, Yasaman Bahri, Roman Novak, Jeffrey Pennington, Jascha Sohl-Dickstein

CoRR, 2020

The large learning rate phase of deep learning: the catapult mechanism.

Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer, Jascha Sohl-Dickstein, Guy Gur-Ari

CoRR, 2020

Infinite attention: NNGP and NTK for deep attention networks.

Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein, Roman Novak

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Jascha Sohl-Dickstein, Jeffrey Pennington

CoRR, 2019

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent.

Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz, Yasaman Bahri, Roman Novak, Jascha Sohl-Dickstein, Jeffrey Pennington

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes.

Roman Novak, Lechao Xiao, Yasaman Bahri, Jaehoon Lee, Greg Yang, Jiri Hron, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes.

Roman Novak, Lechao Xiao, Jaehoon Lee, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

CoRR, 2018

Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10, 000-Layer Vanilla Convolutional Neural Networks.

Lechao Xiao, Yasaman Bahri, Jascha Sohl-Dickstein, Samuel S. Schoenholz, Jeffrey Pennington

Proceedings of the 35th International Conference on Machine Learning, 2018

Sensitivity and Generalization in Neural Networks: an Empirical Study.

Roman Novak, Yasaman Bahri, Daniel A. Abolafia, Jeffrey Pennington, Jascha Sohl-Dickstein

Proceedings of the 6th International Conference on Learning Representations, 2018

Deep Neural Networks as Gaussian Processes.

Jaehoon Lee, Yasaman Bahri, Roman Novak, Samuel S. Schoenholz, Jeffrey Pennington, Jascha Sohl-Dickstein

Proceedings of the 6th International Conference on Learning Representations, 2018

2017

Geometry of Neural Network Loss Surfaces via Random Matrix Theory.

Jeffrey Pennington, Yasaman Bahri

Proceedings of the 34th International Conference on Machine Learning, 2017