Computing the Information Content of Trained Neural Networks

1 March 2021

Papers citing "Computing the Information Content of Trained Neural Networks"

10 / 10 papers shown

Title
Learning by Turning: Neural Architecture Aware Optimisation Yang Liu Jeremy Bernstein M. Meister Yisong Yue ODL 102 26 0 14 Feb 2021
Is SGD a Bayesian sampler? Well, almost Chris Mingard Guillermo Valle Pérez Joar Skalse A. Louis BDL 58 52 0 26 Jun 2020
Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent Jaehoon Lee Lechao Xiao S. Schoenholz Yasaman Bahri Roman Novak Jascha Narain Sohl-Dickstein Jeffrey Pennington 211 1,104 0 18 Feb 2019
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 835 0 19 Dec 2018
Neural Tangent Kernel: Convergence and Generalization in Neural Networks Arthur Jacot Franck Gabriel Clément Hongler 267 3,203 0 20 Jun 2018
Deep learning generalizes because the parameter-function map is biased towards simple functions Guillermo Valle Pérez Chico Q. Camargo A. Louis MLT AI4CE 82 231 0 22 May 2018
Deep Image Prior Dmitry Ulyanov Andrea Vedaldi Victor Lempitsky SupR 122 3,156 0 29 Nov 2017
Deep Neural Networks as Gaussian Processes Jaehoon Lee Yasaman Bahri Roman Novak S. Schoenholz Jeffrey Pennington Jascha Narain Sohl-Dickstein UQCV BDL 125 1,093 0 01 Nov 2017
A Closer Look at Memorization in Deep Networks Devansh Arpit Stanislaw Jastrzebski Nicolas Ballas David M. Krueger Emmanuel Bengio ... Tegan Maharaj Asja Fischer Aaron Courville Yoshua Bengio Simon Lacoste-Julien TDI 122 1,818 0 16 Jun 2017
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite Daniel M. Roy 106 815 0 31 Mar 2017