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Elimination of All Bad Local Minima in Deep Learning

Elimination of All Bad Local Minima in Deep Learning

2 January 2019
Kenji Kawaguchi
L. Kaelbling
ArXivPDFHTML

Papers citing "Elimination of All Bad Local Minima in Deep Learning"

31 / 31 papers shown
Title
Gradient Descent Finds Global Minima for Generalizable Deep Neural
  Networks of Practical Sizes
Gradient Descent Finds Global Minima for Generalizable Deep Neural Networks of Practical Sizes
Kenji Kawaguchi
Jiaoyang Huang
ODL
11
57
0
05 Aug 2019
An Improved Analysis of Training Over-parameterized Deep Neural Networks
An Improved Analysis of Training Over-parameterized Deep Neural Networks
Difan Zou
Quanquan Gu
39
230
0
11 Jun 2019
Every Local Minimum Value is the Global Minimum Value of Induced Model
  in Non-convex Machine Learning
Every Local Minimum Value is the Global Minimum Value of Induced Model in Non-convex Machine Learning
Kenji Kawaguchi
Jiaoyang Huang
L. Kaelbling
AAML
49
18
0
07 Apr 2019
Width Provably Matters in Optimization for Deep Linear Neural Networks
Width Provably Matters in Optimization for Deep Linear Neural Networks
S. Du
Wei Hu
44
94
0
24 Jan 2019
Deep Learning for Classical Japanese Literature
Deep Learning for Classical Japanese Literature
Tarin Clanuwat
Mikel Bober-Irizar
A. Kitamoto
Alex Lamb
Kazuaki Yamamoto
David R Ha
57
705
0
03 Dec 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
55
448
0
21 Nov 2018
Effect of Depth and Width on Local Minima in Deep Learning
Effect of Depth and Width on Local Minima in Deep Learning
Kenji Kawaguchi
Jiaoyang Huang
L. Kaelbling
24
55
0
20 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
94
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
61
1,133
0
09 Nov 2018
Depth with Nonlinearity Creates No Bad Local Minima in ResNets
Depth with Nonlinearity Creates No Bad Local Minima in ResNets
Kenji Kawaguchi
Yoshua Bengio
ODL
54
64
0
21 Oct 2018
On the loss landscape of a class of deep neural networks with no bad
  local valleys
On the loss landscape of a class of deep neural networks with no bad local valleys
Quynh N. Nguyen
Mahesh Chandra Mukkamala
Matthias Hein
36
87
0
27 Sep 2018
Learning ReLU Networks on Linearly Separable Data: Algorithm,
  Optimality, and Generalization
Learning ReLU Networks on Linearly Separable Data: Algorithm, Optimality, and Generalization
G. Wang
G. Giannakis
Jie Chen
MLT
34
131
0
14 Aug 2018
Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex
Deep Neural Networks with Multi-Branch Architectures Are Less Non-Convex
Hongyang R. Zhang
Junru Shao
Ruslan Salakhutdinov
53
14
0
06 Jun 2018
Adding One Neuron Can Eliminate All Bad Local Minima
Adding One Neuron Can Eliminate All Bad Local Minima
Shiyu Liang
Ruoyu Sun
Jason D. Lee
R. Srikant
59
89
0
22 May 2018
Are ResNets Provably Better than Linear Predictors?
Are ResNets Provably Better than Linear Predictors?
Ohad Shamir
19
54
0
18 Apr 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
82
268
0
03 Mar 2018
Gradient descent with identity initialization efficiently learns
  positive definite linear transformations by deep residual networks
Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks
Peter L. Bartlett
D. Helmbold
Philip M. Long
53
116
0
16 Feb 2018
Deep linear neural networks with arbitrary loss: All local minima are
  global
Deep linear neural networks with arbitrary loss: All local minima are global
T. Laurent
J. V. Brecht
ODL
12
136
0
05 Dec 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
46
260
0
01 Nov 2017
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time
Learning Neural Networks with Two Nonlinear Layers in Polynomial Time
Surbhi Goel
Adam R. Klivans
29
52
0
18 Sep 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
53
336
0
10 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
18
649
0
28 May 2017
Learning ReLUs via Gradient Descent
Learning ReLUs via Gradient Descent
Mahdi Soltanolkotabi
MLT
48
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
70
284
0
26 Apr 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
26
313
0
26 Feb 2017
Exponentially vanishing sub-optimal local minima in multilayer neural
  networks
Exponentially vanishing sub-optimal local minima in multilayer neural networks
Daniel Soudry
Elad Hoffer
59
97
0
19 Feb 2017
Identity Matters in Deep Learning
Identity Matters in Deep Learning
Moritz Hardt
Tengyu Ma
OOD
34
398
0
14 Nov 2016
Deep Learning without Poor Local Minima
Deep Learning without Poor Local Minima
Kenji Kawaguchi
ODL
55
919
0
23 May 2016
Bayesian Optimization with Exponential Convergence
Bayesian Optimization with Exponential Convergence
Kenji Kawaguchi
L. Kaelbling
Tomás Lozano-Pérez
40
106
0
05 Apr 2016
Provable Methods for Training Neural Networks with Sparse Connectivity
Provable Methods for Training Neural Networks with Sparse Connectivity
Hanie Sedghi
Anima Anandkumar
32
64
0
08 Dec 2014
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
208
1,189
0
30 Nov 2014
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