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Bounding and Counting Linear Regions of Deep Neural Networks

Bounding and Counting Linear Regions of Deep Neural Networks

6 November 2017
Thiago Serra
Christian Tjandraatmadja
Srikumar Ramalingam
    MLT
ArXivPDFHTML

Papers citing "Bounding and Counting Linear Regions of Deep Neural Networks"

50 / 57 papers shown
Title
Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition
Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition
Nathanael Tepakbong
Ding-Xuan Zhou
Xiang Zhou
46
0
0
13 May 2025
Reinforcement learning with combinatorial actions for coupled restless bandits
Reinforcement learning with combinatorial actions for coupled restless bandits
Lily Xu
Bryan Wilder
Elias B. Khalil
Milind Tambe
75
1
0
01 Mar 2025
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
NEAR: A Training-Free Pre-Estimator of Machine Learning Model Performance
Raphael T. Husistein
Markus Reiher
Marco Eckhoff
142
1
0
20 Feb 2025
On Space Folds of ReLU Neural Networks
On Space Folds of ReLU Neural Networks
Michal Lewandowski
Hamid Eghbalzadeh
Bernhard Heinzl
Raphael Pisoni
Bernhard A.Moser
MLT
87
1
0
17 Feb 2025
A Relative Homology Theory of Representation in Neural Networks
A Relative Homology Theory of Representation in Neural Networks
Kosio Beshkov
99
0
0
17 Feb 2025
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
Polyhedral Complex Extraction from ReLU Networks using Edge Subdivision
Arturs Berzins
27
5
0
12 Jun 2023
Learning Prescriptive ReLU Networks
Learning Prescriptive ReLU Networks
Wei-Ju Sun
Asterios Tsiourvas
21
2
0
01 Jun 2023
SkelEx and BoundEx: Natural Visualization of ReLU Neural Networks
SkelEx and BoundEx: Natural Visualization of ReLU Neural Networks
Pawel Pukowski
Haiping Lu
33
0
0
09 May 2023
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
94
33
0
29 Apr 2023
The Power of Typed Affine Decision Structures: A Case Study
The Power of Typed Affine Decision Structures: A Case Study
Gerrit Nolte
Maximilian Schlüter
Alnis Murtovi
Bernhard Steffen
AAML
20
3
0
28 Apr 2023
Algorithmic Recourse with Missing Values
Algorithmic Recourse with Missing Values
Kentaro Kanamori
Takuya Takagi
Ken Kobayashi
Yuichi Ike
31
2
0
28 Apr 2023
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks
  with Soft-Thresholding
Convex Dual Theory Analysis of Two-Layer Convolutional Neural Networks with Soft-Thresholding
Chunyan Xiong
Meng Lu
Xiaotong Yu
JIAN-PENG Cao
Zhong Chen
D. Guo
X. Qu
MLT
40
0
0
14 Apr 2023
Towards Rigorous Understanding of Neural Networks via
  Semantics-preserving Transformations
Towards Rigorous Understanding of Neural Networks via Semantics-preserving Transformations
Maximilian Schlüter
Gerrit Nolte
Alnis Murtovi
Bernhard Steffen
29
6
0
19 Jan 2023
Getting Away with More Network Pruning: From Sparsity to Geometry and
  Linear Regions
Getting Away with More Network Pruning: From Sparsity to Geometry and Linear Regions
Junyang Cai
Khai-Nguyen Nguyen
Nishant Shrestha
Aidan Good
Ruisen Tu
Xin Yu
Shandian Zhe
Thiago Serra
MLT
40
7
0
19 Jan 2023
Effects of Data Geometry in Early Deep Learning
Effects of Data Geometry in Early Deep Learning
Saket Tiwari
George Konidaris
82
7
0
29 Dec 2022
Exponentially Improving the Complexity of Simulating the
  Weisfeiler-Lehman Test with Graph Neural Networks
Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
Anders Aamand
Justin Y. Chen
Piotr Indyk
Shyam Narayanan
R. Rubinfeld
Nicholas Schiefer
Sandeep Silwal
Tal Wagner
39
21
0
06 Nov 2022
Non-Linear Coordination Graphs
Non-Linear Coordination Graphs
Yipeng Kang
Tonghan Wang
Xiao-Ren Wu
Qianlan Yang
Chongjie Zhang
37
9
0
26 Oct 2022
Improved Bounds on Neural Complexity for Representing Piecewise Linear
  Functions
Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions
Kuan-Lin Chen
H. Garudadri
Bhaskar D. Rao
11
19
0
13 Oct 2022
Curved Representation Space of Vision Transformers
Curved Representation Space of Vision Transformers
Juyeop Kim
Junha Park
Songkuk Kim
Jongseok Lee
ViT
41
6
0
11 Oct 2022
Batch Normalization Explained
Batch Normalization Explained
Randall Balestriero
Richard G. Baraniuk
AAML
36
16
0
29 Sep 2022
Why neural networks find simple solutions: the many regularizers of
  geometric complexity
Why neural networks find simple solutions: the many regularizers of geometric complexity
Benoit Dherin
Michael Munn
M. Rosca
David Barrett
57
31
0
27 Sep 2022
Sparse deep neural networks for modeling aluminum electrolysis dynamics
Sparse deep neural networks for modeling aluminum electrolysis dynamics
E. Lundby
Adil Rasheed
I. Halvorsen
J. Gravdahl
29
14
0
13 Sep 2022
On the Number of Regions of Piecewise Linear Neural Networks
On the Number of Regions of Piecewise Linear Neural Networks
Alexis Goujon
Arian Etemadi
M. Unser
44
13
0
17 Jun 2022
Lower and Upper Bounds for Numbers of Linear Regions of Graph
  Convolutional Networks
Lower and Upper Bounds for Numbers of Linear Regions of Graph Convolutional Networks
Hao Chen
Yu Wang
Huan Xiong
GNN
16
6
0
01 Jun 2022
On the Feasibility and Generality of Patch-based Adversarial Attacks on
  Semantic Segmentation Problems
On the Feasibility and Generality of Patch-based Adversarial Attacks on Semantic Segmentation Problems
Soma Kontár
A. Horváth
AAML
35
1
0
21 May 2022
Training Fully Connected Neural Networks is $\exists\mathbb{R}$-Complete
Training Fully Connected Neural Networks is ∃R\exists\mathbb{R}∃R-Complete
Daniel Bertschinger
Christoph Hertrich
Paul Jungeblut
Tillmann Miltzow
Simon Weber
OffRL
61
30
0
04 Apr 2022
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another
  in Neural Networks
The Combinatorial Brain Surgeon: Pruning Weights That Cancel One Another in Neural Networks
Xin Yu
Thiago Serra
Srikumar Ramalingam
Shandian Zhe
42
48
0
09 Mar 2022
Training Thinner and Deeper Neural Networks: Jumpstart Regularization
Training Thinner and Deeper Neural Networks: Jumpstart Regularization
Carles Roger Riera Molina
Camilo Rey
Thiago Serra
Eloi Puertas
O. Pujol
27
4
0
30 Jan 2022
A Simple and Efficient Sampling-based Algorithm for General Reachability
  Analysis
A Simple and Efficient Sampling-based Algorithm for General Reachability Analysis
T. Lew
Lucas Janson
Riccardo Bonalli
Marco Pavone
32
18
0
10 Dec 2021
Unsupervised Representation Learning via Neural Activation Coding
Unsupervised Representation Learning via Neural Activation Coding
Yookoon Park
Sangho Lee
Gunhee Kim
David M. Blei
SSL
23
8
0
07 Dec 2021
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for
  Efficient Object Detection
MAE-DET: Revisiting Maximum Entropy Principle in Zero-Shot NAS for Efficient Object Detection
Zhenhong Sun
Ming Lin
Xiuyu Sun
Zhiyu Tan
Hao Li
Rong Jin
23
32
0
26 Nov 2021
SPINE: Soft Piecewise Interpretable Neural Equations
SPINE: Soft Piecewise Interpretable Neural Equations
Jasdeep Singh Grover
Harsh Minesh Domadia
Rajashree Tapase
Grishma Sharma
19
0
0
20 Nov 2021
Sound and Complete Neural Network Repair with Minimality and Locality
  Guarantees
Sound and Complete Neural Network Repair with Minimality and Locality Guarantees
Feisi Fu
Wenchao Li
KELM
AAML
41
26
0
14 Oct 2021
Convergence of Deep ReLU Networks
Convergence of Deep ReLU Networks
Yuesheng Xu
Haizhang Zhang
37
27
0
27 Jul 2021
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Multifidelity Modeling for Physics-Informed Neural Networks (PINNs)
Michael Penwarden
Shandian Zhe
A. Narayan
Robert M. Kirby
29
43
0
25 Jun 2021
Sharp bounds for the number of regions of maxout networks and vertices
  of Minkowski sums
Sharp bounds for the number of regions of maxout networks and vertices of Minkowski sums
Guido Montúfar
Yue Ren
Leon Zhang
20
39
0
16 Apr 2021
Fast Jacobian-Vector Product for Deep Networks
Fast Jacobian-Vector Product for Deep Networks
Randall Balestriero
Richard Baraniuk
31
4
0
01 Apr 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
25
81
0
21 Dec 2020
Certified Monotonic Neural Networks
Certified Monotonic Neural Networks
Xingchao Liu
Xing Han
Na Zhang
Qiang Liu
24
79
0
20 Nov 2020
On the Number of Linear Functions Composing Deep Neural Network: Towards
  a Refined Definition of Neural Networks Complexity
On the Number of Linear Functions Composing Deep Neural Network: Towards a Refined Definition of Neural Networks Complexity
Yuuki Takai
Akiyoshi Sannai
Matthieu Cordonnier
80
4
0
23 Oct 2020
Continual Learning in Low-rank Orthogonal Subspaces
Continual Learning in Low-rank Orthogonal Subspaces
Arslan Chaudhry
Naeemullah Khan
P. Dokania
Philip Torr
CLL
33
115
0
22 Oct 2020
Influence Functions in Deep Learning Are Fragile
Influence Functions in Deep Learning Are Fragile
S. Basu
Phillip E. Pope
S. Feizi
TDI
37
219
0
25 Jun 2020
The Depth-to-Width Interplay in Self-Attention
The Depth-to-Width Interplay in Self-Attention
Yoav Levine
Noam Wies
Or Sharir
Hofit Bata
Amnon Shashua
30
45
0
22 Jun 2020
In Proximity of ReLU DNN, PWA Function, and Explicit MPC
In Proximity of ReLU DNN, PWA Function, and Explicit MPC
Saman Fahandezh-Saadi
Masayoshi Tomizuka
18
4
0
09 Jun 2020
Provably Good Solutions to the Knapsack Problem via Neural Networks of
  Bounded Size
Provably Good Solutions to the Knapsack Problem via Neural Networks of Bounded Size
Christoph Hertrich
M. Skutella
50
21
0
28 May 2020
Approximation in shift-invariant spaces with deep ReLU neural networks
Approximation in shift-invariant spaces with deep ReLU neural networks
Yunfei Yang
Zhen Li
Yang Wang
34
14
0
25 May 2020
An Outer-approximation Guided Optimization Approach for Constrained
  Neural Network Inverse Problems
An Outer-approximation Guided Optimization Approach for Constrained Neural Network Inverse Problems
Myun-Seok Cheon
8
5
0
24 Feb 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
21
38
0
04 Jan 2020
Lossless Compression of Deep Neural Networks
Lossless Compression of Deep Neural Networks
Thiago Serra
Abhinav Kumar
Srikumar Ramalingam
24
56
0
01 Jan 2020
Is Deeper Better only when Shallow is Good?
Is Deeper Better only when Shallow is Good?
Eran Malach
Shai Shalev-Shwartz
28
45
0
08 Mar 2019
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