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Error bounds for approximations with deep ReLU networks
v1v2v3 (latest)

Error bounds for approximations with deep ReLU networks

3 October 2016
Dmitry Yarotsky
ArXiv (abs)PDFHTML

Papers citing "Error bounds for approximations with deep ReLU networks"

33 / 633 papers shown
On decision regions of narrow deep neural networks
On decision regions of narrow deep neural networksNeural Networks (NN), 2018
H. Beise
S. Cruz
Udo Schröder
MLT
305
18
0
03 Jul 2018
Exponential Convergence of the Deep Neural Network Approximation for
  Analytic Functions
Exponential Convergence of the Deep Neural Network Approximation for Analytic FunctionsScience China Mathematics (Sci China Math), 2018
Weinan E
Qingcan Wang
100
108
0
01 Jul 2018
Bounds on the Approximation Power of Feedforward Neural Networks
Bounds on the Approximation Power of Feedforward Neural NetworksInternational Conference on Machine Learning (ICML), 2018
M. Mehrabi
A. Tchamkerten
Mansoor I. Yousefi
98
12
0
29 Jun 2018
ResNet with one-neuron hidden layers is a Universal Approximator
ResNet with one-neuron hidden layers is a Universal ApproximatorNeural Information Processing Systems (NeurIPS), 2018
Hongzhou Lin
Stefanie Jegelka
278
243
0
28 Jun 2018
Learning One-hidden-layer ReLU Networks via Gradient Descent
Learning One-hidden-layer ReLU Networks via Gradient Descent
Xiao Zhang
Yaodong Yu
Lingxiao Wang
Quanquan Gu
MLT
239
138
0
20 Jun 2018
The universal approximation power of finite-width deep ReLU networks
The universal approximation power of finite-width deep ReLU networks
Dmytro Perekrestenko
Philipp Grohs
Dennis Elbrächter
Helmut Bölcskei
131
37
0
05 Jun 2018
On representation power of neural network-based graph embedding and
  beyond
On representation power of neural network-based graph embedding and beyond
Akifumi Okuno
Hidetoshi Shimodaira
60
2
0
31 May 2018
Representational Power of ReLU Networks and Polynomial Kernels: Beyond
  Worst-Case Analysis
Representational Power of ReLU Networks and Polynomial Kernels: Beyond Worst-Case Analysis
Frederic Koehler
Andrej Risteski
90
12
0
29 May 2018
Universality of Deep Convolutional Neural Networks
Universality of Deep Convolutional Neural Networks
Ding-Xuan Zhou
HAIPINN
651
561
0
28 May 2018
Nonparametric Density Estimation under Adversarial Losses
Nonparametric Density Estimation under Adversarial Losses
Shashank Singh
Ananya Uppal
Boyue Li
Chun-Liang Li
Manzil Zaheer
Barnabás Póczós
GAN
224
62
0
22 May 2018
Butterfly-Net: Optimal Function Representation Based on Convolutional
  Neural Networks
Butterfly-Net: Optimal Function Representation Based on Convolutional Neural Networks
Yingzhou Li
Xiuyuan Cheng
Jianfeng Lu
341
23
0
18 May 2018
A comparison of deep networks with ReLU activation function and linear
  spline-type methods
A comparison of deep networks with ReLU activation function and linear spline-type methods
Konstantin Eckle
Johannes Schmidt-Hieber
245
354
0
06 Apr 2018
Posterior Concentration for Sparse Deep Learning
Posterior Concentration for Sparse Deep Learning
Nicholas G. Polson
Veronika Rockova
UQCVBDL
319
99
0
24 Mar 2018
Neural Networks Should Be Wide Enough to Learn Disconnected Decision
  Regions
Neural Networks Should Be Wide Enough to Learn Disconnected Decision RegionsInternational Conference on Machine Learning (ICML), 2018
Quynh N. Nguyen
Mahesh Chandra Mukkamala
Matthias Hein
MLT
319
56
0
28 Feb 2018
A probabilistic framework for multi-view feature learning with
  many-to-many associations via neural networks
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno
Tetsuya Hada
Hidetoshi Shimodaira
142
15
0
13 Feb 2018
Deep Neural Networks Learn Non-Smooth Functions Effectively
Deep Neural Networks Learn Non-Smooth Functions Effectively
Masaaki Imaizumi
Kenji Fukumizu
308
136
0
13 Feb 2018
On the Universal Approximability and Complexity Bounds of Quantized ReLU
  Neural Networks
On the Universal Approximability and Complexity Bounds of Quantized ReLU Neural Networks
Yukun Ding
Jinglan Liu
Jinjun Xiong
Yiyu Shi
MQ
232
24
0
10 Feb 2018
Optimal approximation of continuous functions by very deep ReLU networks
Optimal approximation of continuous functions by very deep ReLU networks
Dmitry Yarotsky
375
318
0
10 Feb 2018
How Well Can Generative Adversarial Networks Learn Densities: A
  Nonparametric View
How Well Can Generative Adversarial Networks Learn Densities: A Nonparametric View
Tengyuan Liang
GAN
201
41
0
21 Dec 2017
Lower bounds over Boolean inputs for deep neural networks with ReLU
  gates
Lower bounds over Boolean inputs for deep neural networks with ReLU gates
Anirbit Mukherjee
A. Basu
217
23
0
08 Nov 2017
Approximating Continuous Functions by ReLU Nets of Minimal Width
Approximating Continuous Functions by ReLU Nets of Minimal Width
Boris Hanin
Mark Sellke
225
263
0
31 Oct 2017
Optimization Landscape and Expressivity of Deep CNNs
Optimization Landscape and Expressivity of Deep CNNsInternational Conference on Learning Representations (ICLR), 2017
Quynh N. Nguyen
Matthias Hein
269
29
0
30 Oct 2017
Optimal approximation of piecewise smooth functions using deep ReLU
  neural networks
Optimal approximation of piecewise smooth functions using deep ReLU neural networks
P. Petersen
Felix Voigtländer
563
513
0
15 Sep 2017
The Expressive Power of Neural Networks: A View from the Width
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu
Hongming Pu
Feicheng Wang
Zhiqiang Hu
Liwei Wang
509
969
0
08 Sep 2017
Nonparametric regression using deep neural networks with ReLU activation
  function
Nonparametric regression using deep neural networks with ReLU activation function
Johannes Schmidt-Hieber
637
928
0
22 Aug 2017
Universal Function Approximation by Deep Neural Nets with Bounded Width
  and ReLU Activations
Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations
Boris Hanin
366
378
0
09 Aug 2017
Neural networks and rational functions
Neural networks and rational functionsInternational Conference on Machine Learning (ICML), 2017
Matus Telgarsky
141
94
0
11 Jun 2017
Optimal Approximation with Sparsely Connected Deep Neural Networks
Optimal Approximation with Sparsely Connected Deep Neural Networks
Helmut Bölcskei
Philipp Grohs
Gitta Kutyniok
P. Petersen
380
272
0
04 May 2017
Quantified advantage of discontinuous weight selection in approximations
  with deep neural networks
Quantified advantage of discontinuous weight selection in approximations with deep neural networks
Dmitry Yarotsky
39
11
0
03 May 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
628
485
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
639
704
0
04 Nov 2016
Depth-Width Tradeoffs in Approximating Natural Functions with Neural
  Networks
Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Itay Safran
Ohad Shamir
297
180
0
31 Oct 2016
Why Deep Neural Networks for Function Approximation?
Why Deep Neural Networks for Function Approximation?International Conference on Learning Representations (ICLR), 2016
Shiyu Liang
R. Srikant
259
401
0
13 Oct 2016
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