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. 2104.08135
  4. Cited By
Sharp bounds for the number of regions of maxout networks and vertices
  of Minkowski sums
v1v2 (latest)

Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums

16 April 2021
Guido Montúfar
Yue Ren
Leon Zhang
ArXiv (abs)PDFHTML

Papers citing "Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums"

27 / 27 papers shown
Title
Neural Networks and (Virtual) Extended Formulations
Neural Networks and (Virtual) Extended Formulations
Christoph Hertrich
Georg Loho
117
3
0
05 Nov 2024
When Deep Learning Meets Polyhedral Theory: A Survey
When Deep Learning Meets Polyhedral Theory: A Survey
Joey Huchette
Gonzalo Muñoz
Thiago Serra
Calvin Tsay
AI4CE
154
37
0
29 Apr 2023
Weisfeiler and Lehman Go Topological: Message Passing Simplicial
  Networks
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks
Cristian Bodnar
Fabrizio Frasca
Yu Guang Wang
N. Otter
Guido Montúfar
Pietro Lio
M. Bronstein
109
257
0
04 Mar 2021
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for
  Deep ReLU Networks
Tight Bounds on the Smallest Eigenvalue of the Neural Tangent Kernel for Deep ReLU Networks
Quynh N. Nguyen
Marco Mondelli
Guido Montúfar
76
83
0
21 Dec 2020
On the Number of Linear Regions of Convolutional Neural Networks
On the Number of Linear Regions of Convolutional Neural Networks
Huan Xiong
Lei Huang
Mengyang Yu
Li Liu
Fan Zhu
Ling Shao
MLT
61
68
0
01 Jun 2020
Empirical Studies on the Properties of Linear Regions in Deep Neural
  Networks
Empirical Studies on the Properties of Linear Regions in Deep Neural Networks
Xiao Zhang
Dongrui Wu
50
38
0
04 Jan 2020
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Boris Hanin
David Rolnick
90
228
0
03 Jun 2019
The Geometry of Deep Networks: Power Diagram Subdivision
The Geometry of Deep Networks: Power Diagram Subdivision
Randall Balestriero
Romain Cosentino
B. Aazhang
Richard Baraniuk
AI4CE
57
64
0
21 May 2019
A Sober Look at Neural Network Initializations
A Sober Look at Neural Network Initializations
Ingo Steinwart
36
9
0
27 Mar 2019
Complexity of Linear Regions in Deep Networks
Complexity of Linear Regions in Deep Networks
Boris Hanin
David Rolnick
49
234
0
25 Jan 2019
Empirical Bounds on Linear Regions of Deep Rectifier Networks
Empirical Bounds on Linear Regions of Deep Rectifier Networks
Thiago Serra
Srikumar Ramalingam
65
42
0
08 Oct 2018
A Framework for the construction of upper bounds on the number of affine
  linear regions of ReLU feed-forward neural networks
A Framework for the construction of upper bounds on the number of affine linear regions of ReLU feed-forward neural networks
Peter Hinz
Sara van de Geer
61
23
0
05 Jun 2018
A Tropical Approach to Neural Networks with Piecewise Linear Activations
A Tropical Approach to Neural Networks with Piecewise Linear Activations
Vasileios Charisopoulos
Petros Maragos
60
40
0
22 May 2018
Tropical Geometry of Deep Neural Networks
Tropical Geometry of Deep Neural Networks
Liwen Zhang
Gregory Naitzat
Lek-Heng Lim
87
140
0
18 May 2018
How to Start Training: The Effect of Initialization and Architecture
How to Start Training: The Effect of Initialization and Architecture
Boris Hanin
David Rolnick
91
256
0
05 Mar 2018
Bounding and Counting Linear Regions of Deep Neural Networks
Bounding and Counting Linear Regions of Deep Neural Networks
Thiago Serra
Christian Tjandraatmadja
Srikumar Ramalingam
MLT
67
251
0
06 Nov 2017
Nearly-tight VC-dimension and pseudodimension bounds for piecewise
  linear neural networks
Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks
Peter L. Bartlett
Nick Harvey
Christopher Liaw
Abbas Mehrabian
220
434
0
08 Mar 2017
Understanding Deep Neural Networks with Rectified Linear Units
Understanding Deep Neural Networks with Rectified Linear Units
R. Arora
A. Basu
Poorya Mianjy
Anirbit Mukherjee
PINN
164
643
0
04 Nov 2016
Error bounds for approximations with deep ReLU networks
Error bounds for approximations with deep ReLU networks
Dmitry Yarotsky
198
1,236
0
03 Oct 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
84
791
0
16 Jun 2016
Benefits of depth in neural networks
Benefits of depth in neural networks
Matus Telgarsky
380
609
0
14 Feb 2016
The Power of Depth for Feedforward Neural Networks
The Power of Depth for Feedforward Neural Networks
Ronen Eldan
Ohad Shamir
228
732
0
12 Dec 2015
Representation Benefits of Deep Feedforward Networks
Representation Benefits of Deep Feedforward Networks
Matus Telgarsky
86
242
0
27 Sep 2015
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
  ImageNet Classification
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
Kaiming He
Xinming Zhang
Shaoqing Ren
Jian Sun
VLM
350
18,654
0
06 Feb 2015
On the Number of Linear Regions of Deep Neural Networks
On the Number of Linear Regions of Deep Neural Networks
Guido Montúfar
Razvan Pascanu
Kyunghyun Cho
Yoshua Bengio
96
1,256
0
08 Feb 2014
On the number of response regions of deep feed forward networks with
  piece-wise linear activations
On the number of response regions of deep feed forward networks with piece-wise linear activations
Razvan Pascanu
Guido Montúfar
Yoshua Bengio
FAtt
124
257
0
20 Dec 2013
Maxout Networks
Maxout Networks
Ian Goodfellow
David Warde-Farley
M. Berk Mirza
Aaron Courville
Yoshua Bengio
OOD
264
2,179
0
18 Feb 2013
1