1.3k citations · 2.5k across the 28 of their papers we have counts for
12 papers · 1 filter
Infinite attention: NNGP and NTK for deep attention networks
Jiri Hron, Yasaman Bahri, Jascha Sohl-Dickstein +1
There is a growing amount of literature on the relationship between wide neural networks (NNs) and Gaussian processes (GPs), identifying an equivalence between the two for a variet…
Exact posterior distributions of wide Bayesian neural networks
Jiri Hron, Yasaman Bahri, Roman Novak +2
Recent work has shown that the prior over functions induced by a deep Bayesian neural network (BNN) behaves as a Gaussian process (GP) as the width of all layers becomes large. How…
The large learning rate phase of deep learning: the catapult mechanism
Aitor Lewkowycz, Yasaman Bahri, Ethan Dyer +2
The choice of initial learning rate can have a profound effect on the performance of deep networks. We present a class of neural networks with solvable training dynamics, and confi…
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…
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…
Eliminating all bad Local Minima from Loss Landscapes without even adding an Extra Unit
Jascha Sohl-Dickstein, Kenji Kawaguchi
Recent work has noted that all bad local minima can be removed from neural network loss landscapes, by adding a single unit with a particular parameterization. We show that the cor…