Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning

5 March 2018

Papers citing "Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning"

7 / 7 papers shown

Title
The Interpolating Information Criterion for Overparameterized Models Liam Hodgkinson Christopher van der Heide Roberto Salomone Fred Roosta Michael W. Mahoney 20 7 0 15 Jul 2023
Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances Marcel Kühn B. Rosenow 21 3 0 08 Jun 2023
Dissecting the Effects of SGD Noise in Distinct Regimes of Deep Learning Antonio Sclocchi Mario Geiger M. Wyart 40 6 0 31 Jan 2023
Deep Learning is Singular, and That's Good Daniel Murfet Susan Wei Biwei Huang Hui Li Jesse Gell-Redman T. Quella UQCV 24 26 0 22 Oct 2020
Dimensionality compression and expansion in Deep Neural Networks Stefano Recanatesi M. Farrell Madhu S. Advani Timothy Moore Guillaume Lajoie E. Shea-Brown 26 72 0 02 Jun 2019
Deep learning generalizes because the parameter-function map is biased towards simple functions Guillermo Valle Pérez Chico Q. Camargo A. Louis MLT AI4CE 18 226 0 22 May 2018
Three Factors Influencing Minima in SGD Stanislaw Jastrzebski Zachary Kenton Devansh Arpit Nicolas Ballas Asja Fischer Yoshua Bengio Amos Storkey 42 457 0 13 Nov 2017