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Gradient Descent Finds Global Minima of Deep Neural Networks
v1v2v3v4 (latest)

Gradient Descent Finds Global Minima of Deep Neural Networks

9 November 2018
S. Du
Jason D. Lee
Haochuan Li
Liwei Wang
Masayoshi Tomizuka
    ODL
ArXiv (abs)PDFHTML

Papers citing "Gradient Descent Finds Global Minima of Deep Neural Networks"

16 / 466 papers shown
Title
On the Benefit of Width for Neural Networks: Disappearance of Bad Basins
On the Benefit of Width for Neural Networks: Disappearance of Bad Basins
Dawei Li
Tian Ding
Ruoyu Sun
128
38
0
28 Dec 2018
Overparameterized Nonlinear Learning: Gradient Descent Takes the
  Shortest Path?
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
On Lazy Training in Differentiable Programming
Lénaïc Chizat
Edouard Oyallon
Francis R. Bach
111
840
0
19 Dec 2018
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
A Frank-Wolfe Framework for Efficient and Effective Adversarial Attacks
Jinghui Chen
Dongruo Zhou
Jinfeng Yi
Quanquan Gu
AAML
90
68
0
27 Nov 2018
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
254
448
0
21 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
AI4CEODL
304
1,470
0
09 Nov 2018
Learning Two Layer Rectified Neural Networks in Polynomial Time
Learning Two Layer Rectified Neural Networks in Polynomial Time
Ainesh Bakshi
Rajesh Jayaram
David P. Woodruff
NoLa
178
70
0
05 Nov 2018
Small ReLU networks are powerful memorizers: a tight analysis of
  memorization capacity
Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity
Chulhee Yun
S. Sra
Ali Jadbabaie
153
118
0
17 Oct 2018
Regularization Matters: Generalization and Optimization of Neural Nets
  v.s. their Induced Kernel
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
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
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
118
183
0
09 Sep 2018
Collapse of Deep and Narrow Neural Nets
Collapse of Deep and Narrow Neural Nets
Lu Lu
Yanhui Su
George Karniadakis
ODL
96
156
0
15 Aug 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent
Learning One-hidden-layer ReLU Networks via Gradient Descent
Xiao Zhang
Yaodong Yu
Lingxiao Wang
Quanquan Gu
MLT
129
135
0
20 Jun 2018
Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross
  Entropy
Guaranteed Recovery of One-Hidden-Layer Neural Networks via Cross Entropy
H. Fu
Yuejie Chi
Yingbin Liang
FedML
93
39
0
18 Feb 2018
Spurious Valleys in Two-layer Neural Network Optimization Landscapes
Spurious Valleys in Two-layer Neural Network Optimization Landscapes
Luca Venturi
Afonso S. Bandeira
Joan Bruna
82
74
0
18 Feb 2018
Small nonlinearities in activation functions create bad local minima in
  neural networks
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
New insights and perspectives on the natural gradient method
New insights and perspectives on the natural gradient method
James Martens
ODL
198
631
0
03 Dec 2014
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