Geometry of Neural Network Loss Surfaces via Random Matrix Theory. - Jeffrey Pennington; Yasaman Bahri (ICML, 2017)

Deep Neural Networks as Gaussian Processes. - Jaehoon Lee 001; Yasaman Bahri; Roman Novak; Samuel S. Schoenholz; Jeffrey Pennington; Jascha Sohl-Dickstein (ICLR (Poster), 2018)

Sensitivity and Generalization in Neural Networks: an Empirical Study. - Roman Novak; Yasaman Bahri; Daniel A. Abolafia; Jeffrey Pennington; Jascha Sohl-Dickstein (ICLR (Poster), 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 (ICML, 2018)

Bayesian Convolutional Neural Networks with Many Channels are Gaussian Processes. - Roman Novak; Lechao Xiao; Jaehoon Lee 001; Yasaman Bahri; Daniel A. Abolafia; Jeffrey Pennington; Jascha Sohl-Dickstein (CoRR, 2018)

Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes. - Roman Novak; Lechao Xiao; Yasaman Bahri; Jaehoon Lee 001; Greg Yang; Jiri Hron; Daniel A. Abolafia; Jeffrey Pennington; Jascha Sohl-Dickstein (ICLR (Poster), 2019)

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. - Jaehoon Lee 001; Lechao Xiao; Samuel S. Schoenholz; Yasaman Bahri; Roman Novak; Jascha Sohl-Dickstein; Jeffrey Pennington (NeurIPS, 2019)

Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent. - Jaehoon Lee 001; Lechao Xiao; Samuel S. Schoenholz; Yasaman Bahri; Jascha Sohl-Dickstein; Jeffrey Pennington (CoRR, 2019)

Infinite attention: NNGP and NTK for deep attention networks. - Jiri Hron; Yasaman Bahri; Jascha Sohl-Dickstein; Roman Novak (ICML, 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)

Exact posterior distributions of wide Bayesian neural networks. - Jiri Hron; Yasaman Bahri; Roman Novak; Jeffrey Pennington; Jascha Sohl-Dickstein (CoRR, 2020)

Explaining Neural Scaling Laws. - Yasaman Bahri; Ethan Dyer; Jared Kaplan; Jaehoon Lee 001; Utkarsh Sharma (CoRR, 2021)

The Evolution of Out-of-Distribution Robustness Throughout Fine-Tuning. - Anders Johan Andreassen; Yasaman Bahri; Behnam Neyshabur; Rebecca Roelofs (Trans. Mach. Learn. Res., 2022)

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)

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models. - Aarohi Srivastava; Abhinav Rastogi; Abhishek Rao; Abu Awal Md Shoeb; Abubakar Abid; Adam Fisch; Adam R. Brown; Adam Santoro; Aditya Gupta; Adrià Garriga-Alonso; Agnieszka Kluska; Aitor Lewkowycz; et al. (Trans. Mach. Learn. Res., 2023)

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)

CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning. - Hao Cui; Zahra Shamsi; Gowoon Cheon; Xuejian Ma; Shutong Li; Maria Tikhanovskaya; Peter Christian Norgaard; Nayantara Mudur; Martyna Beata Plomecka; Paul Raccuglia; Yasaman Bahri; Victor V. Albert; et al. (ICLR, 2025)

Solvable Dynamics of Self-Supervised Word Embeddings and the Emergence of Analogical Reasoning. - Dhruva Karkada; James B. Simon; Yasaman Bahri; Michael Robert DeWeese (CoRR, 2025)

CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning. - Hao Cui; Zahra Shamsi; Gowoon Cheon; Xuejian Ma; Shutong Li; Maria Tikhanovskaya; Peter C. Norgaard; Nayantara Mudur; Martyna Plomecka; Paul Raccuglia; Yasaman Bahri; Victor V. Albert; et al. (CoRR, 2025)

On the Emergence of Linear Analogies in Word Embeddings. - Daniel J. Korchinski; Dhruva Karkada; Yasaman Bahri; Matthieu Wyart (CoRR, 2025)