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Learning One-hidden-layer ReLU Networks via Gradient Descent

Learning One-hidden-layer ReLU Networks via Gradient Descent

20 June 2018
Xiao Zhang
Yaodong Yu
Lingxiao Wang
Quanquan Gu
    MLT
ArXivPDFHTML

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

50 / 56 papers shown
Title
Nonparametric Learning of Two-Layer ReLU Residual Units
Nonparametric Learning of Two-Layer ReLU Residual Units
Zhunxuan Wang
Linyun He
Chunchuan Lyu
Shay B. Cohen
MLT
OffRL
77
1
0
17 Aug 2020
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU
  Networks
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
Difan Zou
Yuan Cao
Dongruo Zhou
Quanquan Gu
ODL
96
448
0
21 Nov 2018
Learning and Generalization in Overparameterized Neural Networks, Going
  Beyond Two Layers
Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers
Zeyuan Allen-Zhu
Yuanzhi Li
Yingyu Liang
MLT
94
769
0
12 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization
A Convergence Theory for Deep Learning via Over-Parameterization
Zeyuan Allen-Zhu
Yuanzhi Li
Zhao Song
AI4CE
ODL
141
1,457
0
09 Nov 2018
Gradient Descent Finds Global Minima of Deep Neural Networks
Gradient Descent Finds Global Minima of Deep Neural Networks
S. Du
Jason D. Lee
Haochuan Li
Liwei Wang
Masayoshi Tomizuka
ODL
98
1,133
0
09 Nov 2018
Learning Two-layer Neural Networks with Symmetric Inputs
Learning Two-layer Neural Networks with Symmetric Inputs
Rong Ge
Rohith Kuditipudi
Zhize Li
Xiang Wang
OOD
MLT
82
57
0
16 Oct 2018
Learning One-hidden-layer Neural Networks under General Input
  Distributions
Learning One-hidden-layer Neural Networks under General Input Distributions
Weihao Gao
Ashok Vardhan Makkuva
Sewoong Oh
Pramod Viswanath
MLT
47
28
0
09 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
Gradient Descent Provably Optimizes Over-parameterized Neural Networks
S. Du
Xiyu Zhai
Barnabás Póczós
Aarti Singh
MLT
ODL
114
1,261
0
04 Oct 2018
Learning Overparameterized Neural Networks via Stochastic Gradient
  Descent on Structured Data
Learning Overparameterized Neural Networks via Stochastic Gradient Descent on Structured Data
Yuanzhi Li
Yingyu Liang
MLT
96
652
0
03 Aug 2018
On the Power of Over-parametrization in Neural Networks with Quadratic
  Activation
On the Power of Over-parametrization in Neural Networks with Quadratic Activation
S. Du
Jason D. Lee
93
268
0
03 Mar 2018
Optimal approximation of continuous functions by very deep ReLU networks
Optimal approximation of continuous functions by very deep ReLU networks
Dmitry Yarotsky
87
293
0
10 Feb 2018
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Spurious Local Minima are Common in Two-Layer ReLU Neural Networks
Itay Safran
Ohad Shamir
101
263
0
24 Dec 2017
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of
  Spurious Local Minima
Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of Spurious Local Minima
S. Du
Jason D. Lee
Yuandong Tian
Barnabás Póczós
Aarti Singh
MLT
117
236
0
03 Dec 2017
Lower bounds over Boolean inputs for deep neural networks with ReLU
  gates
Lower bounds over Boolean inputs for deep neural networks with ReLU gates
Anirbit Mukherjee
A. Basu
36
21
0
08 Nov 2017
Learning One-hidden-layer Neural Networks with Landscape Design
Learning One-hidden-layer Neural Networks with Landscape Design
Rong Ge
Jason D. Lee
Tengyu Ma
MLT
86
260
0
01 Nov 2017
Approximating Continuous Functions by ReLU Nets of Minimal Width
Approximating Continuous Functions by ReLU Nets of Minimal Width
Boris Hanin
Mark Sellke
72
232
0
31 Oct 2017
When is a Convolutional Filter Easy To Learn?
When is a Convolutional Filter Easy To Learn?
S. Du
Jason D. Lee
Yuandong Tian
MLT
43
130
0
18 Sep 2017
The Expressive Power of Neural Networks: A View from the Width
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu
Hongming Pu
Feicheng Wang
Zhiqiang Hu
Liwei Wang
56
886
0
08 Sep 2017
Universal Function Approximation by Deep Neural Nets with Bounded Width
  and ReLU Activations
Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Boris Hanin
34
354
0
09 Aug 2017
Theoretical insights into the optimization landscape of
  over-parameterized shallow neural networks
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
Mahdi Soltanolkotabi
Adel Javanmard
Jason D. Lee
82
417
0
16 Jul 2017
Global optimality conditions for deep neural networks
Global optimality conditions for deep neural networks
Chulhee Yun
S. Sra
Ali Jadbabaie
134
118
0
08 Jul 2017
Recovery Guarantees for One-hidden-layer Neural Networks
Recovery Guarantees for One-hidden-layer Neural Networks
Kai Zhong
Zhao Song
Prateek Jain
Peter L. Bartlett
Inderjit S. Dhillon
MLT
84
336
0
10 Jun 2017
Weight Sharing is Crucial to Succesful Optimization
Weight Sharing is Crucial to Succesful Optimization
Shai Shalev-Shwartz
Ohad Shamir
Shaked Shammah
59
12
0
02 Jun 2017
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Convergence Analysis of Two-layer Neural Networks with ReLU Activation
Yuanzhi Li
Yang Yuan
MLT
55
649
0
28 May 2017
Learning ReLUs via Gradient Descent
Learning ReLUs via Gradient Descent
Mahdi Soltanolkotabi
MLT
56
181
0
10 May 2017
The loss surface of deep and wide neural networks
The loss surface of deep and wide neural networks
Quynh N. Nguyen
Matthias Hein
ODL
72
284
0
26 Apr 2017
Failures of Gradient-Based Deep Learning
Failures of Gradient-Based Deep Learning
Shai Shalev-Shwartz
Ohad Shamir
Shaked Shammah
ODL
UQCV
61
200
0
23 Mar 2017
How to Escape Saddle Points Efficiently
How to Escape Saddle Points Efficiently
Chi Jin
Rong Ge
Praneeth Netrapalli
Sham Kakade
Michael I. Jordan
ODL
118
834
0
02 Mar 2017
An Analytical Formula of Population Gradient for two-layered ReLU
  network and its Applications in Convergence and Critical Point Analysis
An Analytical Formula of Population Gradient for two-layered ReLU network and its Applications in Convergence and Critical Point Analysis
Yuandong Tian
MLT
72
216
0
02 Mar 2017
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs
Globally Optimal Gradient Descent for a ConvNet with Gaussian Inputs
Alon Brutzkus
Amir Globerson
MLT
63
313
0
26 Feb 2017
Provable learning of Noisy-or Networks
Provable learning of Noisy-or Networks
Sanjeev Arora
Rong Ge
Tengyu Ma
Andrej Risteski
44
26
0
28 Dec 2016
Reliably Learning the ReLU in Polynomial Time
Reliably Learning the ReLU in Polynomial Time
Surbhi Goel
Varun Kanade
Adam R. Klivans
J. Thaler
53
126
0
30 Nov 2016
Identity Matters in Deep Learning
Identity Matters in Deep Learning
Moritz Hardt
Tengyu Ma
OOD
43
398
0
14 Nov 2016
Understanding deep learning requires rethinking generalization
Understanding deep learning requires rethinking generalization
Chiyuan Zhang
Samy Bengio
Moritz Hardt
Benjamin Recht
Oriol Vinyals
HAI
226
4,612
0
10 Nov 2016
Diverse Neural Network Learns True Target Functions
Diverse Neural Network Learns True Target Functions
Bo Xie
Yingyu Liang
Le Song
96
137
0
09 Nov 2016
Error bounds for approximations with deep ReLU networks
Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky
102
1,226
0
03 Oct 2016
Distribution-Specific Hardness of Learning Neural Networks
Distribution-Specific Hardness of Learning Neural Networks
Ohad Shamir
40
116
0
05 Sep 2016
The Landscape of Empirical Risk for Non-convex Losses
The Landscape of Empirical Risk for Non-convex Losses
Song Mei
Yu Bai
Andrea Montanari
26
312
0
22 Jul 2016
Exponential expressivity in deep neural networks through transient chaos
Exponential expressivity in deep neural networks through transient chaos
Ben Poole
Subhaneil Lahiri
M. Raghu
Jascha Narain Sohl-Dickstein
Surya Ganguli
71
587
0
16 Jun 2016
On the Expressive Power of Deep Neural Networks
On the Expressive Power of Deep Neural Networks
M. Raghu
Ben Poole
Jon M. Kleinberg
Surya Ganguli
Jascha Narain Sohl-Dickstein
42
780
0
16 Jun 2016
No bad local minima: Data independent training error guarantees for
  multilayer neural networks
No bad local minima: Data independent training error guarantees for multilayer neural networks
Daniel Soudry
Y. Carmon
88
235
0
26 May 2016
Deep Learning without Poor Local Minima
Deep Learning without Poor Local Minima
Kenji Kawaguchi
ODL
99
919
0
23 May 2016
Toward Deeper Understanding of Neural Networks: The Power of
  Initialization and a Dual View on Expressivity
Toward Deeper Understanding of Neural Networks: The Power of Initialization and a Dual View on Expressivity
Amit Daniely
Roy Frostig
Y. Singer
60
343
0
18 Feb 2016
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
266
605
0
14 Feb 2016
Learning Halfspaces and Neural Networks with Random Initialization
Learning Halfspaces and Neural Networks with Random Initialization
Yuchen Zhang
Jason D. Lee
Martin J. Wainwright
Michael I. Jordan
34
35
0
25 Nov 2015
Expressiveness of Rectifier Networks
Expressiveness of Rectifier Networks
Xingyuan Pan
Vivek Srikumar
OffRL
39
46
0
18 Nov 2015
On the Quality of the Initial Basin in Overspecified Neural Networks
On the Quality of the Initial Basin in Overspecified Neural Networks
Itay Safran
Ohad Shamir
39
127
0
13 Nov 2015
On the Expressive Power of Deep Learning: A Tensor Analysis
On the Expressive Power of Deep Learning: A Tensor Analysis
Nadav Cohen
Or Sharir
Amnon Shashua
52
469
0
16 Sep 2015
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor
  Decomposition
Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition
Rong Ge
Furong Huang
Chi Jin
Yang Yuan
105
1,056
0
06 Mar 2015
Provable Methods for Training Neural Networks with Sparse Connectivity
Provable Methods for Training Neural Networks with Sparse Connectivity
Hanie Sedghi
Anima Anandkumar
38
64
0
08 Dec 2014
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