Gradient descent aligns the layers of deep linear networks

4 October 2018

Papers citing "Gradient descent aligns the layers of deep linear networks"

11 / 61 papers shown

Title
Neural Similarity Learning Weiyang Liu Zhen Liu James M. Rehg Le Song 27 29 0 28 Oct 2019
Gradient Descent Maximizes the Margin of Homogeneous Neural Networks Kaifeng Lyu Jian Li 52 322 0 13 Jun 2019
Kernel and Rich Regimes in Overparametrized Models Blake E. Woodworth Suriya Gunasekar Pedro H. P. Savarese E. Moroshko Itay Golan J. Lee Daniel Soudry Nathan Srebro 30 352 0 13 Jun 2019
Implicit Regularization in Deep Matrix Factorization Sanjeev Arora Nadav Cohen Wei Hu Yuping Luo AI4CE 38 491 0 31 May 2019
Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models Mor Shpigel Nacson Suriya Gunasekar J. Lee Nathan Srebro Daniel Soudry 33 92 0 17 May 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 47 351 0 27 Mar 2019
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 40 961 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 21 94 0 24 Jan 2019
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 33 446 0 21 Nov 2018
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks Ohad Shamir 35 45 0 23 Sep 2018
High-dimensional dynamics of generalization error in neural networks Madhu S. Advani Andrew M. Saxe AI4CE 78 464 0 10 Oct 2017