Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data

3 August 2018

Papers citing "Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data"

26 / 176 papers shown

Title
Analysis of the Gradient Descent Algorithm for a Deep Neural Network Model with Skip-connections E. Weinan Chao Ma Qingcan Wang Lei Wu MLT 37 22 0 10 Apr 2019
On the Power and Limitations of Random Features for Understanding Neural Networks Gilad Yehudai Ohad Shamir MLT 28 181 0 01 Apr 2019
Many Task Learning with Task Routing Gjorgji Strezoski Nanne van Noord M. Worring MoE 24 96 0 28 Mar 2019
Gradient Descent with Early Stopping is Provably Robust to Label Noise for Overparameterized Neural Networks Mingchen Li Mahdi Soltanolkotabi Samet Oymak NoLa 49 351 0 27 Mar 2019
A Priori Estimates of the Population Risk for Residual Networks E. Weinan Chao Ma Qingcan Wang UQCV 40 61 0 06 Mar 2019
Implicit Regularization in Over-parameterized Neural Networks M. Kubo Ryotaro Banno Hidetaka Manabe Masataka Minoji 27 23 0 05 Mar 2019
Uniform convergence may be unable to explain generalization in deep learning Vaishnavh Nagarajan J. Zico Kolter MoMe AI4CE 17 310 0 13 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 55 961 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 26 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
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers Zeyuan Allen-Zhu Yuanzhi Li Yingyu Liang MLT 41 765 0 12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 72 1,448 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 44 1,126 0 09 Nov 2018
On the Convergence Rate of Training Recurrent Neural Networks Zeyuan Allen-Zhu Yuanzhi Li Zhao Song 32 191 0 29 Oct 2018
Subgradient Descent Learns Orthogonal Dictionaries Yu Bai Qijia Jiang Ju Sun 20 51 0 25 Oct 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 31 117 0 17 Oct 2018
Learning Two-layer Neural Networks with Symmetric Inputs Rong Ge Rohith Kuditipudi Zhize Li Xiang Wang OOD MLT 36 57 0 16 Oct 2018
A Priori Estimates of the Population Risk for Two-layer Neural Networks Weinan E Chao Ma Lei Wu 29 130 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 28 245 0 12 Oct 2018
Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem Alon Brutzkus Amir Globerson MLT 19 7 0 06 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 65 1,252 0 04 Oct 2018
Analysis of the Generalization Error: Empirical Risk Minimization over Deep Artificial Neural Networks Overcomes the Curse of Dimensionality in the Numerical Approximation of Black-Scholes Partial Differential Equations Julius Berner Philipp Grohs Arnulf Jentzen 14 181 0 09 Sep 2018
Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization G. Wang G. Giannakis Jie Chen MLT 24 131 0 14 Aug 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent Xiao Zhang Yaodong Yu Lingxiao Wang Quanquan Gu MLT 33 134 0 20 Jun 2018
When Will Gradient Methods Converge to Max-margin Classifier under ReLU Models? Tengyu Xu Yi Zhou Kaiyi Ji Yingbin Liang 31 19 0 12 Jun 2018
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima N. Keskar Dheevatsa Mudigere J. Nocedal M. Smelyanskiy P. T. P. Tang ODL 310 2,896 0 15 Sep 2016