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Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem

Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem

9 December 2019
Vaggos Chatziafratis
Sai Ganesh Nagarajan
Ioannis Panageas
Tianlin Li
ArXiv (abs)PDFHTML

Papers citing "Depth-Width Trade-offs for ReLU Networks via Sharkovsky's Theorem"

15 / 15 papers shown
Title
Newton vs the machine: solving the chaotic three-body problem using deep
  neural networks
Newton vs the machine: solving the chaotic three-body problem using deep neural networks
Philip G. Breen
Christopher N. Foley
Tjarda Boekholt
Simon Portegies Zwart
AI4CE
67
73
0
16 Oct 2019
On the Expressive Power of Deep Polynomial Neural Networks
On the Expressive Power of Deep Polynomial Neural Networks
Joe Kileel
Matthew Trager
Joan Bruna
66
83
0
29 May 2019
Is Deeper Better only when Shallow is Good?
Is Deeper Better only when Shallow is Good?
Eran Malach
Shai Shalev-Shwartz
59
45
0
08 Mar 2019
Collapse of Deep and Narrow Neural Nets
Collapse of Deep and Narrow Neural Nets
Lu Lu
Yanhui Su
George Karniadakis
ODL
79
156
0
15 Aug 2018
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
166
643
0
04 Nov 2016
Why Deep Neural Networks for Function Approximation?
Why Deep Neural Networks for Function Approximation?
Shiyu Liang
R. Srikant
140
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
94
596
0
16 Jun 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
230
732
0
12 Dec 2015
Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two
  and Depth-Three Threshold Circuits
Super-Linear Gate and Super-Quadratic Wire Lower Bounds for Depth-Two and Depth-Three Threshold Circuits
D. Kane
Ryan Williams
55
61
0
24 Nov 2015
Representation Benefits of Deep Feedforward Networks
Representation Benefits of Deep Feedforward Networks
Matus Telgarsky
88
242
0
27 Sep 2015
Adam: A Method for Stochastic Optimization
Adam: A Method for Stochastic Optimization
Diederik P. Kingma
Jimmy Ba
ODL
2.1K
150,433
0
22 Dec 2014
On the Expressive Efficiency of Sum Product Networks
On the Expressive Efficiency of Sum Product Networks
James Martens
Venkatesh Medabalimi
TPM
76
68
0
27 Nov 2014
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
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