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Depth separation beyond radial functions

Depth separation beyond radial functions

2 February 2021
Luca Venturi
Samy Jelassi
Tristan Ozuch
Joan Bruna
ArXivPDFHTML

Papers citing "Depth separation beyond radial functions"

16 / 16 papers shown
Title
Spectral complexity of deep neural networks
Spectral complexity of deep neural networks
Simmaco Di Lillo
Domenico Marinucci
Michele Salvi
Stefano Vigogna
BDL
103
2
0
15 May 2024
On the Approximation Power of Two-Layer Networks of Random ReLUs
On the Approximation Power of Two-Layer Networks of Random ReLUs
Daniel J. Hsu
Clayton Sanford
Rocco A. Servedio
Emmanouil-Vasileios Vlatakis-Gkaragkounis
48
25
0
03 Feb 2021
Depth separation for reduced deep networks in nonlinear model reduction:
  Distilling shock waves in nonlinear hyperbolic problems
Depth separation for reduced deep networks in nonlinear model reduction: Distilling shock waves in nonlinear hyperbolic problems
Donsub Rim
Luca Venturi
Joan Bruna
Benjamin Peherstorfer
72
9
0
28 Jul 2020
Expressivity of Deep Neural Networks
Expressivity of Deep Neural Networks
Ingo Gühring
Mones Raslan
Gitta Kutyniok
64
51
0
09 Jul 2020
Neural Networks with Small Weights and Depth-Separation Barriers
Neural Networks with Small Weights and Depth-Separation Barriers
Gal Vardi
Ohad Shamir
45
18
0
31 May 2020
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem
Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem
Vaggos Chatziafratis
Sai Ganesh Nagarajan
Ioannis Panageas
Tianlin Li
44
21
0
09 Dec 2019
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Boris Hanin
David Rolnick
81
227
0
03 Jun 2019
On the Spectral Bias of Neural Networks
On the Spectral Bias of Neural Networks
Nasim Rahaman
A. Baratin
Devansh Arpit
Felix Dräxler
Min Lin
Fred Hamprecht
Yoshua Bengio
Aaron Courville
152
1,439
0
22 Jun 2018
Optimal Approximation with Sparsely Connected Deep Neural Networks
Optimal Approximation with Sparsely Connected Deep Neural Networks
Helmut Bölcskei
Philipp Grohs
Gitta Kutyniok
P. Petersen
197
255
0
04 May 2017
Depth Separation for Neural Networks
Depth Separation for Neural Networks
Amit Daniely
MDE
37
74
0
27 Feb 2017
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of
  Dimensionality: a Review
Why and When Can Deep -- but Not Shallow -- Networks Avoid the Curse of Dimensionality: a Review
T. Poggio
H. Mhaskar
Lorenzo Rosasco
Brando Miranda
Q. Liao
97
576
0
02 Nov 2016
Why Deep Neural Networks for Function Approximation?
Why Deep Neural Networks for Function Approximation?
Shiyu Liang
R. Srikant
132
385
0
13 Oct 2016
Exponential expressivity in deep neural networks through transient chaos
Exponential expressivity in deep neural networks through transient chaos
Ben Poole
Subhaneil Lahiri
M. Raghu
Jascha Narain Sohl-Dickstein
Surya Ganguli
90
591
0
16 Jun 2016
The Power of Depth for Feedforward Neural Networks
The Power of Depth for Feedforward Neural Networks
Ronen Eldan
Ohad Shamir
216
732
0
12 Dec 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
88
1,254
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
117
257
0
20 Dec 2013
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