A Geometric Approach of Gradient Descent Algorithms in Linear Neural Networks

8 November 2018

Papers citing "A Geometric Approach of Gradient Descent Algorithms in Linear Neural Networks"

5 / 5 papers shown

Title
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss Pierre Bréchet Katerina Papagiannouli Jing An Guido Montúfar 41 3 0 06 Mar 2023
The loss landscape of deep linear neural networks: a second-order analysis El Mehdi Achour Franccois Malgouyres Sébastien Gerchinovitz ODL 26 9 0 28 Jul 2021
Convergence analysis for gradient flows in the training of artificial neural networks with ReLU activation Arnulf Jentzen Adrian Riekert 27 23 0 09 Jul 2021
First-order Methods Almost Always Avoid Saddle Points Jason D. Lee Ioannis Panageas Georgios Piliouras Max Simchowitz Michael I. Jordan Benjamin Recht ODL 95 83 0 20 Oct 2017
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 186 1,186 0 30 Nov 2014