Schoenholz, Samuel S.

12 publications

ICML 2022 Deep Equilibrium Networks Are Sensitive to Initialization Statistics Atish Agarwala, Samuel S Schoenholz

ICML 2022 Fast Finite Width Neural Tangent Kernel Roman Novak, Jascha Sohl-Dickstein, Samuel S Schoenholz

ICML 2021 Learn2Hop: Learned Optimization on Rough Landscapes Amil Merchant, Luke Metz, Samuel S Schoenholz, Ekin D Cubuk

ICML 2021 Tilting the Playing Field: Dynamical Loss Functions for Machine Learning Miguel Ruiz-Garcia, Ge Zhang, Samuel S Schoenholz, Andrea J. Liu

ICML 2021 Whitening and Second Order Optimization Both Make Information in the Dataset Unusable During Training, and Can Reduce or Prevent Generalization Neha Wadia, Daniel Duckworth, Samuel S Schoenholz, Ethan Dyer, Jascha Sohl-Dickstein

ICLR 2020 Neural Tangents: Fast and Easy Infinite Neural Networks in Python Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Alexander A. Alemi, Jascha Sohl-Dickstein, Samuel S. Schoenholz

ICLR 2019 A Mean Field Theory of Batch Normalization Greg Yang, Jeffrey Pennington, Vinay Rao, Jascha Sohl-Dickstein, Samuel S. Schoenholz

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

AISTATS 2018 The Emergence of Spectral Universality in Deep Networks Jeffrey Pennington, Samuel S. Schoenholz, Surya Ganguli

ICLR 2017 Deep Information Propagation Samuel S. Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein

ICLR 2017 Explaining the Learning Dynamics of Direct Feedback Alignment Justin Gilmer, Colin Raffel, Samuel S. Schoenholz, Maithra Raghu, Jascha Sohl-Dickstein

ICML 2017 Neural Message Passing for Quantum Chemistry Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, Oriol Vinyals, George E. Dahl