Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU
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Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks

21 November 2018

Quanquan Gu

Papers citing "Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks"

11 / 111 papers shown

Title
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
LipschitzLR: Using theoretically computed adaptive learning rates for fast convergence Rahul Yedida Snehanshu Saha Tejas Prashanth ODL 22 12 0 20 Feb 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 37 962 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 18 93 0 24 Jan 2019
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du J. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 41 1,122 0 09 Nov 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 23 117 0 17 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei J. Lee Qiang Liu Tengyu Ma 23 243 0 12 Oct 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent Xiao Zhang Yaodong Yu Lingxiao Wang Quanquan Gu MLT 28 134 0 20 Jun 2018
Benefits of depth in neural networks Matus Telgarsky 148 602 0 14 Feb 2016
Norm-Based Capacity Control in Neural Networks Behnam Neyshabur Ryota Tomioka Nathan Srebro 125 577 0 27 Feb 2015
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 183 1,185 0 30 Nov 2014