3.8k citations · 7.6k across the 48 of their papers we have counts for
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Neural Tangents: Fast and Easy Infinite Neural Networks in Python
Roman Novak, Lechao Xiao, Jiri Hron +4
Neural Tangents is a library designed to enable research into infinite-width neural networks. It provides a high-level API for specifying complex and hierarchical neural network ar…
Disentangling Trainability and Generalization in Deep Neural Networks
Lechao Xiao, Jeffrey Pennington, Samuel S. Schoenholz
A longstanding goal in the theory of deep learning is to characterize the conditions under which a given neural network architecture will be trainable, and if so, how well it might…
JAX, M.D.: A Framework for Differentiable Physics
Samuel S. Schoenholz, Ekin D. Cubuk
We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics. JAX MD includes a number of physics simulation environ…
A Mean Field Theory of Batch Normalization
Greg Yang, Jeffrey Pennington, Vinay Rao +2
We develop a mean field theory for batch normalization in fully-connected feedforward neural networks. In so doing, we provide a precise characterization of signal propagation and…
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent
Jaehoon Lee, Lechao Xiao, Samuel S. Schoenholz +4
A longstanding goal in deep learning research has been to precisely characterize training and generalization. However, the often complex loss landscapes of neural networks have mad…
Dynamical Isometry and a Mean Field Theory of LSTMs and GRUs
Dar Gilboa, Bo Chang, Minmin Chen +4
Training recurrent neural networks (RNNs) on long sequence tasks is plagued with difficulties arising from the exponential explosion or vanishing of signals as they propagate forwa…