Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks

3 January 2022

Papers citing "Complexity from Adaptive-Symmetries Breaking: Global Minima in the Statistical Mechanics of Deep Neural Networks"

6 / 6 papers shown

Title
Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges M. Bronstein Joan Bruna Taco S. Cohen Petar Velivcković GNN 172 1,103 0 27 Apr 2021
Scaling Laws for Neural Language Models Jared Kaplan Sam McCandlish T. Henighan Tom B. Brown B. Chess R. Child Scott Gray Alec Radford Jeff Wu Dario Amodei 226 4,453 0 23 Jan 2020
Why bigger is not always better: on finite and infinite neural networks Laurence Aitchison 173 51 0 17 Oct 2019
Global optimality conditions for deep neural networks Chulhee Yun S. Sra Ali Jadbabaie 121 117 0 08 Jul 2017
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 252 9,134 0 06 Jun 2015
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 175 1,185 0 30 Nov 2014