Learning One-hidden-layer ReLU Networks via Gradient Descent

20 June 2018

Quanquan Gu

Papers citing "Learning One-hidden-layer ReLU Networks via Gradient Descent"

36 / 86 papers shown

Title
Directional Pruning of Deep Neural Networks Shih-Kang Chao Zhanyu Wang Yue Xing Guang Cheng ODL 6 33 0 16 Jun 2020
Agnostic Learning of a Single Neuron with Gradient Descent Spencer Frei Yuan Cao Quanquan Gu MLT 16 59 0 29 May 2020
Feature Purification: How Adversarial Training Performs Robust Deep Learning Zeyuan Allen-Zhu Yuanzhi Li MLT AAML 27 146 0 20 May 2020
Piecewise linear activations substantially shape the loss surfaces of neural networks Fengxiang He Bohan Wang Dacheng Tao ODL 20 28 0 27 Mar 2020
Tune smarter not harder: A principled approach to tuning learning rates for shallow nets Thulasi Tholeti Sheetal Kalyani 13 4 0 22 Mar 2020
On the Global Convergence of Training Deep Linear ResNets Difan Zou Philip M. Long Quanquan Gu 18 37 0 02 Mar 2020
A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks Zixiang Chen Yuan Cao Quanquan Gu Tong Zhang MLT 19 10 0 10 Feb 2020
Sharp Rate of Convergence for Deep Neural Network Classifiers under the Teacher-Student Setting Tianyang Hu Zuofeng Shang Guang Cheng 19 19 0 19 Jan 2020
Optimization for deep learning: theory and algorithms Ruoyu Sun ODL 14 168 0 19 Dec 2019
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks Yuan Cao Quanquan Gu MLT 12 19 0 12 Nov 2019
Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals Surbhi Goel Sushrut Karmalkar Adam R. Klivans 10 53 0 04 Nov 2019
Growing axons: greedy learning of neural networks with application to function approximation Daria Fokina Ivan V. Oseledets 9 18 0 28 Oct 2019
Nearly Minimal Over-Parametrization of Shallow Neural Networks Armin Eftekhari Chaehwan Song V. Cevher 8 1 0 09 Oct 2019
Theoretical Issues in Deep Networks: Approximation, Optimization and Generalization T. Poggio Andrzej Banburski Q. Liao ODL 21 161 0 25 Aug 2019
An Improved Analysis of Training Over-parameterized Deep Neural Networks Difan Zou Quanquan Gu 16 230 0 11 Jun 2019
Fast Convergence of Natural Gradient Descent for Overparameterized Neural Networks Guodong Zhang James Martens Roger C. Grosse ODL 6 124 0 27 May 2019
Gradient Descent can Learn Less Over-parameterized Two-layer Neural Networks on Classification Problems Atsushi Nitanda Geoffrey Chinot Taiji Suzuki MLT 8 33 0 23 May 2019
Theory III: Dynamics and Generalization in Deep Networks Andrzej Banburski Q. Liao Brando Miranda Lorenzo Rosasco Fernanda De La Torre Jack Hidary T. Poggio AI4CE 19 3 0 12 Mar 2019
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks Yuan Cao Quanquan Gu ODL MLT AI4CE 9 155 0 04 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 20 961 0 24 Jan 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks S. Du Wei Hu 11 93 0 24 Jan 2019
Fitting ReLUs via SGD and Quantized SGD Seyed Mohammadreza Mousavi Kalan Mahdi Soltanolkotabi A. Avestimehr 8 24 0 19 Jan 2019
Convex Relaxations of Convolutional Neural Nets Burak Bartan Mert Pilanci 12 5 0 31 Dec 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks Difan Zou Yuan Cao Dongruo Zhou Quanquan Gu ODL 11 446 0 21 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks S. Du J. Lee Haochuan Li Liwei Wang M. Tomizuka ODL 13 1,120 0 09 Nov 2018
Subgradient Descent Learns Orthogonal Dictionaries Yu Bai Qijia Jiang Ju Sun 10 51 0 25 Oct 2018
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity Chulhee Yun S. Sra Ali Jadbabaie 13 117 0 17 Oct 2018
Learning Two-layer Neural Networks with Symmetric Inputs Rong Ge Rohith Kuditipudi Zhize Li Xiang Wang OOD MLT 25 57 0 16 Oct 2018
Learning One-hidden-layer Neural Networks under General Input Distributions Weihao Gao Ashok Vardhan Makkuva Sewoong Oh Pramod Viswanath MLT 25 28 0 09 Oct 2018
Efficiently testing local optimality and escaping saddles for ReLU networks Chulhee Yun S. Sra Ali Jadbabaie 22 10 0 28 Sep 2018
Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization G. Wang G. Giannakis Jie Chen MLT 22 131 0 14 Aug 2018
Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy H. Fu Yuejie Chi Yingbin Liang FedML 11 39 0 18 Feb 2018
Small nonlinearities in activation functions create bad local minima in neural networks Chulhee Yun S. Sra Ali Jadbabaie ODL 15 93 0 10 Feb 2018
Global optimality conditions for deep neural networks Chulhee Yun S. Sra Ali Jadbabaie 121 117 0 08 Jul 2017
Benefits of depth in neural networks Matus Telgarsky 125 602 0 14 Feb 2016
The Loss Surfaces of Multilayer Networks A. Choromańska Mikael Henaff Michaël Mathieu Gerard Ben Arous Yann LeCun ODL 177 1,185 0 30 Nov 2014