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Gradient Descent Provably Optimizes Over-parameterized Neural Networks

4 October 2018

Aarti Singh

Papers citing "Gradient Descent Provably Optimizes Over-parameterized Neural Networks"

32 / 882 papers shown

Title
Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks Sanjeev Arora S. Du Wei Hu Zhiyuan Li Ruosong Wang MLT 234 974 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 115 95 0 24 Jan 2019
Understanding Geometry of Encoder-Decoder CNNs J. C. Ye Woon Kyoung Sung 3DV AI4CE 88 74 0 22 Jan 2019
Training Neural Networks as Learning Data-adaptive Kernels: Provable Representation and Approximation Benefits Xialiang Dou Tengyuan Liang MLT 83 42 0 21 Jan 2019
Scaling description of generalization with number of parameters in deep learning Mario Geiger Arthur Jacot S. Spigler Franck Gabriel Levent Sagun Stéphane dÁscoli Giulio Biroli Clément Hongler Matthieu Wyart 112 196 0 06 Jan 2019
Analysis of a Two-Layer Neural Network via Displacement Convexity Adel Javanmard Marco Mondelli Andrea Montanari MLT 119 57 0 05 Jan 2019
A Theoretical Analysis of Deep Q-Learning Jianqing Fan Zhuoran Yang Yuchen Xie Zhaoran Wang 203 611 0 01 Jan 2019
On the Benefit of Width for Neural Networks: Disappearance of Bad Basins Dawei Li Tian Ding Ruoyu Sun 133 39 0 28 Dec 2018
Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? Samet Oymak Mahdi Soltanolkotabi ODL 73 177 0 25 Dec 2018
On Lazy Training in Differentiable Programming Lénaïc Chizat Edouard Oyallon Francis R. Bach 111 840 0 19 Dec 2018
Deep Geometric Prior for Surface Reconstruction Francis Williams T. Schneider Claudio Silva Denis Zorin Joan Bruna Daniele Panozzo 3DPC 130 191 0 27 Nov 2018
Forward Stability of ResNet and Its Variants Linan Zhang Hayden Schaeffer 121 48 0 24 Nov 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 257 448 0 21 Nov 2018
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 237 775 0 12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 306 1,471 0 09 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du Jason D. Lee Haochuan Li Liwei Wang Masayoshi Tomizuka ODL 350 1,137 0 09 Nov 2018
A Geometric Approach of Gradient Descent Algorithms in Linear Neural Networks S. Mahabadi Zhenyu Liao Romain Couillet 50 13 0 08 Nov 2018
On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu Yuanzhi Li Zhao Song 253 193 0 29 Oct 2018
Subgradient Descent Learns Orthogonal Dictionaries Yu Bai Qijia Jiang Ju Sun 165 51 0 25 Oct 2018
A Modern Take on the Bias-Variance Tradeoff in Neural Networks Brady Neal Sarthak Mittal A. Baratin Vinayak Tantia Matthew Scicluna Simon Lacoste-Julien Ioannis Mitliagkas 87 168 0 19 Oct 2018
Exchangeability and Kernel Invariance in Trained MLPs Russell Tsuchida Fred Roosta M. Gallagher 25 3 0 19 Oct 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 160 118 0 17 Oct 2018
Learning Two-layer Neural Networks with Symmetric Inputs Rong Ge Rohith Kuditipudi Zhize Li Xiang Wang OOD MLT 201 59 0 16 Oct 2018
A Priori Estimates of the Population Risk for Two-layer Neural Networks Weinan E Chao Ma Lei Wu 101 132 0 15 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel Colin Wei Jason D. Lee Qiang Liu Tengyu Ma 268 245 0 12 Oct 2018
Unbiased deep solvers for linear parametric PDEs Marc Sabate Vidales David Siska Lukasz Szpruch OOD 65 8 0 11 Oct 2018
Efficiently testing local optimality and escaping saddles for ReLU networks Chulhee Yun S. Sra Ali Jadbabaie 90 10 0 28 Sep 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent Xiao Zhang Yaodong Yu Lingxiao Wang Quanquan Gu MLT 129 135 0 20 Jun 2018
Compressed Sensing with Deep Image Prior and Learned Regularization Dave Van Veen A. Jalal Mahdi Soltanolkotabi Eric Price S. Vishwanath A. Dimakis 139 182 0 17 Jun 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 402 3,494 0 09 Mar 2018
Small nonlinearities in activation functions create bad local minima in neural networks Chulhee Yun S. Sra Ali Jadbabaie ODL 125 95 0 10 Feb 2018
Global Guarantees for Enforcing Deep Generative Priors by Empirical Risk Paul Hand V. Voroninski UQCV 177 138 0 22 May 2017