Finite Versus Infinite Neural Networks: an Empirical Study

Finite Versus Infinite Neural Networks: an Empirical Study

31 July 2020

Jeffrey Pennington

Jascha Narain Sohl-Dickstein

Papers citing "Finite Versus Infinite Neural Networks: an Empirical Study"

13 / 63 papers shown

Title
Dataset Meta-Learning from Kernel Ridge-Regression Timothy Nguyen Zhourung Chen Jaehoon Lee DD 36 240 0 30 Oct 2020
Are wider nets better given the same number of parameters? A. Golubeva Behnam Neyshabur Guy Gur-Ari 27 44 0 27 Oct 2020
A Dynamical View on Optimization Algorithms of Overparameterized Neural Networks Zhiqi Bu Shiyun Xu Kan Chen 33 17 0 25 Oct 2020
Stable ResNet Soufiane Hayou Eugenio Clerico Bo He George Deligiannidis Arnaud Doucet Judith Rousseau ODL SSeg 46 51 0 24 Oct 2020
Whitening and second order optimization both make information in the dataset unusable during training, and can reduce or prevent generalization Neha S. Wadia Daniel Duckworth S. Schoenholz Ethan Dyer Jascha Narain Sohl-Dickstein 27 13 0 17 Aug 2020
Predicting the outputs of finite deep neural networks trained with noisy gradients Gadi Naveh Oded Ben-David H. Sompolinsky Zohar Ringel 19 20 0 02 Apr 2020
Scalable Uncertainty for Computer Vision with Functional Variational Inference Eduardo D C Carvalho R. Clark Andrea Nicastro Paul H. J. Kelly BDL UQCV 152 22 0 06 Mar 2020
The large learning rate phase of deep learning: the catapult mechanism Aitor Lewkowycz Yasaman Bahri Ethan Dyer Jascha Narain Sohl-Dickstein Guy Gur-Ari ODL 159 234 0 04 Mar 2020
Self-Distillation Amplifies Regularization in Hilbert Space H. Mobahi Mehrdad Farajtabar Peter L. Bartlett 19 226 0 13 Feb 2020
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 175 51 0 17 Oct 2019
Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks Lechao Xiao Yasaman Bahri Jascha Narain Sohl-Dickstein S. Schoenholz Jeffrey Pennington 233 348 0 14 Jun 2018
Towards A Rigorous Science of Interpretable Machine Learning Finale Doshi-Velez Been Kim XAI FaML 257 3,690 0 28 Feb 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 285 9,145 0 06 Jun 2015