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Depth Creates No Bad Local Minima

Depth Creates No Bad Local Minima

27 February 2017
Haihao Lu
Kenji Kawaguchi
    ODL
    FAtt
ArXivPDFHTML

Papers citing "Depth Creates No Bad Local Minima"

22 / 22 papers shown
Title
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach
System Identification and Control Using Lyapunov-Based Deep Neural Networks without Persistent Excitation: A Concurrent Learning Approach
Rebecca G. Hart
Omkar Sudhir Patil
Zachary I. Bell
Warren E. Dixon
14
0
0
15 May 2025
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
Mean-Field Analysis for Learning Subspace-Sparse Polynomials with Gaussian Input
Ziang Chen
Rong Ge
MLT
61
1
0
10 Jan 2025
Function Space and Critical Points of Linear Convolutional Networks
Function Space and Critical Points of Linear Convolutional Networks
Kathlén Kohn
Guido Montúfar
Vahid Shahverdi
Matthew Trager
26
11
0
12 Apr 2023
Critical Points and Convergence Analysis of Generative Deep Linear
  Networks Trained with Bures-Wasserstein Loss
Critical Points and Convergence Analysis of Generative Deep Linear Networks Trained with Bures-Wasserstein Loss
Pierre Bréchet
Katerina Papagiannouli
Jing An
Guido Montúfar
33
3
0
06 Mar 2023
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work
When Expressivity Meets Trainability: Fewer than nnn Neurons Can Work
Jiawei Zhang
Yushun Zhang
Mingyi Hong
Ruoyu Sun
Zhi-Quan Luo
29
10
0
21 Oct 2022
Exact Solutions of a Deep Linear Network
Exact Solutions of a Deep Linear Network
Liu Ziyin
Botao Li
Xiangmin Meng
ODL
19
21
0
10 Feb 2022
The loss landscape of deep linear neural networks: a second-order
  analysis
The loss landscape of deep linear neural networks: a second-order analysis
El Mehdi Achour
Franccois Malgouyres
Sébastien Gerchinovitz
ODL
24
9
0
28 Jul 2021
Optimization for deep learning: theory and algorithms
Optimization for deep learning: theory and algorithms
Ruoyu Sun
ODL
25
168
0
19 Dec 2019
Weight-space symmetry in deep networks gives rise to permutation
  saddles, connected by equal-loss valleys across the loss landscape
Weight-space symmetry in deep networks gives rise to permutation saddles, connected by equal-loss valleys across the loss landscape
Johanni Brea
Berfin Simsek
Bernd Illing
W. Gerstner
23
55
0
05 Jul 2019
Interpretable Few-Shot Learning via Linear Distillation
Interpretable Few-Shot Learning via Linear Distillation
Arip Asadulaev
Igor Kuznetsov
Andrey Filchenkov
FedML
FAtt
11
1
0
13 Jun 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
21
94
0
24 Jan 2019
Non-attracting Regions of Local Minima in Deep and Wide Neural Networks
Non-attracting Regions of Local Minima in Deep and Wide Neural Networks
Henning Petzka
C. Sminchisescu
29
9
0
16 Dec 2018
Gradient descent aligns the layers of deep linear networks
Gradient descent aligns the layers of deep linear networks
Ziwei Ji
Matus Telgarsky
30
248
0
04 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
16
87
0
27 Sep 2018
Exponential Convergence Time of Gradient Descent for One-Dimensional
  Deep Linear Neural Networks
Exponential Convergence Time of Gradient Descent for One-Dimensional Deep Linear Neural Networks
Ohad Shamir
35
45
0
23 Sep 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
39
14
0
06 Jun 2018
Understanding Batch Normalization
Understanding Batch Normalization
Johan Bjorck
Carla P. Gomes
B. Selman
Kilian Q. Weinberger
21
593
0
01 Jun 2018
Mad Max: Affine Spline Insights into Deep Learning
Mad Max: Affine Spline Insights into Deep Learning
Randall Balestriero
Richard Baraniuk
AI4CE
31
78
0
17 May 2018
The Global Optimization Geometry of Shallow Linear Neural Networks
The Global Optimization Geometry of Shallow Linear Neural Networks
Zhihui Zhu
Daniel Soudry
Yonina C. Eldar
M. Wakin
ODL
18
36
0
13 May 2018
Visualizing the Loss Landscape of Neural Nets
Visualizing the Loss Landscape of Neural Nets
Hao Li
Zheng Xu
Gavin Taylor
Christoph Studer
Tom Goldstein
106
1,848
0
28 Dec 2017
Global optimality conditions for deep neural networks
Global optimality conditions for deep neural networks
Chulhee Yun
S. Sra
Ali Jadbabaie
128
117
0
08 Jul 2017
The Loss Surfaces of Multilayer Networks
The Loss Surfaces of Multilayer Networks
A. Choromańska
Mikael Henaff
Michaël Mathieu
Gerard Ben Arous
Yann LeCun
ODL
183
1,185
0
30 Nov 2014
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