ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1712.10132
  4. Cited By
The Multilinear Structure of ReLU Networks

The Multilinear Structure of ReLU Networks

29 December 2017
T. Laurent
J. V. Brecht
ArXivPDFHTML

Papers citing "The Multilinear Structure of ReLU Networks"

17 / 17 papers shown
Title
SGD Learns Over-parameterized Networks that Provably Generalize on
  Linearly Separable Data
SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
Alon Brutzkus
Amir Globerson
Eran Malach
Shai Shalev-Shwartz
MLT
120
277
0
27 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
34
130
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
The Landscape of Deep Learning Algorithms
The Landscape of Deep Learning Algorithms
Pan Zhou
Jiashi Feng
35
24
0
19 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
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
33
216
0
02 Mar 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
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Entropy-SGD: Biasing Gradient Descent Into Wide Valleys
Pratik Chaudhari
A. Choromańska
Stefano Soatto
Yann LeCun
Carlo Baldassi
C. Borgs
J. Chayes
Levent Sagun
R. Zecchina
ODL
57
769
0
06 Nov 2016
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp
  Minima
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima
N. Keskar
Dheevatsa Mudigere
J. Nocedal
M. Smelyanskiy
P. T. P. Tang
ODL
335
2,913
0
15 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
18
312
0
22 Jul 2016
Deep Learning without Poor Local Minima
Deep Learning without Poor Local Minima
Kenji Kawaguchi
ODL
55
919
0
23 May 2016
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
31
127
0
13 Nov 2015
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
Identifying and attacking the saddle point problem in high-dimensional
  non-convex optimization
Identifying and attacking the saddle point problem in high-dimensional non-convex optimization
Yann N. Dauphin
Razvan Pascanu
Çağlar Gülçehre
Kyunghyun Cho
Surya Ganguli
Yoshua Bengio
ODL
73
1,379
0
10 Jun 2014
Complex-Valued Autoencoders
Complex-Valued Autoencoders
Pierre Baldi
Zhiqin Lu
53
59
0
20 Aug 2011
1