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Universal Function Approximation by Deep Neural Nets with Bounded Width
  and ReLU Activations
v1v2v3 (latest)

Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations

9 August 2017
Boris Hanin
ArXiv (abs)PDFHTML

Papers citing "Universal Function Approximation by Deep Neural Nets with Bounded Width and ReLU Activations"

50 / 161 papers shown
Error estimate for a universal function approximator of ReLU network
  with a local connection
Error estimate for a universal function approximator of ReLU network with a local connection
Jaeyeon Kang
Sunghwan Moon
77
0
0
03 Sep 2020
A deep network construction that adapts to intrinsic dimensionality
  beyond the domain
A deep network construction that adapts to intrinsic dimensionality beyond the domain
A. Cloninger
T. Klock
AI4CE
362
14
0
06 Aug 2020
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network
  Based Vector-to-Vector Regression
Analyzing Upper Bounds on Mean Absolute Errors for Deep Neural Network Based Vector-to-Vector Regression
Jun Qi
Jun Du
Sabato Marco Siniscalchi
Xiaoli Ma
Chin-Hui Lee
202
44
0
04 Aug 2020
Multi-Task Learning for Multi-Dimensional Regression: Application to
  Luminescence Sensing
Multi-Task Learning for Multi-Dimensional Regression: Application to Luminescence SensingApplied Sciences (AS), 2019
Umberto
Umberto Michelucci
F. Venturini
AI4CE
80
20
0
27 Jul 2020
Interpretable, Multidimensional, Multimodal Anomaly Detection with
  Negative Sampling for Detection of Device Failure
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device FailureInternational Conference on Machine Learning (ICML), 2020
John Sipple
137
59
0
12 Jul 2020
Expressivity of Deep Neural Networks
Expressivity of Deep Neural Networks
Ingo Gühring
Mones Raslan
Gitta Kutyniok
213
61
0
09 Jul 2020
Is SGD a Bayesian sampler? Well, almost
Is SGD a Bayesian sampler? Well, almost
Chris Mingard
Guillermo Valle Pérez
Joar Skalse
A. Louis
BDL
285
64
0
26 Jun 2020
NPLIC: A Machine Learning Approach to Piecewise Linear Interface
  Construction
NPLIC: A Machine Learning Approach to Piecewise Linear Interface Construction
M. Ataei
M. Bussmann
V. Shaayegan
F. Costa
Sejin Han
Chul B. Park
AI4CE
179
24
0
26 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
102
5
0
09 Jun 2020
On the Number of Linear Regions of Convolutional Neural Networks
On the Number of Linear Regions of Convolutional Neural NetworksInternational Conference on Machine Learning (ICML), 2020
Huan Xiong
Lei Huang
Mengyang Yu
Li Liu
Fan Zhu
Ling Shao
MLT
267
73
0
01 Jun 2020
Influence via Ethos: On the Persuasive Power of Reputation in
  Deliberation Online
Influence via Ethos: On the Persuasive Power of Reputation in Deliberation OnlineManagement Sciences (MS), 2020
Emaad Manzoor
George H. Chen
Dokyun Lee
Michael D. Smith
106
12
0
01 Jun 2020
The Expressive Power of a Class of Normalizing Flow Models
The Expressive Power of a Class of Normalizing Flow ModelsInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2020
Zhifeng Kong
Kamalika Chaudhuri
TPM
168
53
0
31 May 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 SizeAAAI Conference on Artificial Intelligence (AAAI), 2020
Christoph Hertrich
M. Skutella
382
28
0
28 May 2020
Physically interpretable machine learning algorithm on multidimensional
  non-linear fields
Physically interpretable machine learning algorithm on multidimensional non-linear fields
Rem-Sophia Mouradi
C. Goeury
O. Thual
F. Zaoui
P. Tassi
OOD
192
7
0
28 May 2020
Approximation in shift-invariant spaces with deep ReLU neural networks
Approximation in shift-invariant spaces with deep ReLU neural networksNeural Networks (NN), 2020
Yunfei Yang
Zhen Li
Yang Wang
244
14
0
25 May 2020
Safe Learning-based Observers for Unknown Nonlinear Systems using
  Bayesian Optimization
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization
Ankush Chakrabarty
M. Benosman
146
17
0
12 May 2020
Overall error analysis for the training of deep neural networks via
  stochastic gradient descent with random initialisation
Overall error analysis for the training of deep neural networks via stochastic gradient descent with random initialisationApplied Mathematics and Computation (Appl. Math. Comput.), 2020
Arnulf Jentzen
Timo Welti
167
20
0
03 Mar 2020
A closer look at the approximation capabilities of neural networks
A closer look at the approximation capabilities of neural networksInternational Conference on Learning Representations (ICLR), 2020
Kai Fong Ernest Chong
79
18
0
16 Feb 2020
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I
  Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets
Gauthier Gidel
David Balduzzi
Wojciech M. Czarnecki
M. Garnelo
Yoram Bachrach
265
7
0
14 Feb 2020
On transfer learning of neural networks using bi-fidelity data for
  uncertainty propagation
On transfer learning of neural networks using bi-fidelity data for uncertainty propagationInternational Journal for Uncertainty Quantification (IJUQ), 2020
Subhayan De
Jolene Britton
Matthew J. Reynolds
Ryan W. Skinner
Kenneth Jansen
Alireza Doostan
159
53
0
11 Feb 2020
Totally Deep Support Vector Machines
Totally Deep Support Vector Machines
H. Sahbi
107
2
0
12 Dec 2019
Robust Training and Initialization of Deep Neural Networks: An Adaptive
  Basis Viewpoint
Robust Training and Initialization of Deep Neural Networks: An Adaptive Basis ViewpointMathematical and Scientific Machine Learning (MSML), 2019
E. Cyr
Mamikon A. Gulian
Ravi G. Patel
M. Perego
N. Trask
216
84
0
10 Dec 2019
Neural Contextual Bandits with UCB-based Exploration
Neural Contextual Bandits with UCB-based Exploration
Dongruo Zhou
Lihong Li
Quanquan Gu
423
16
0
11 Nov 2019
A Numerical Investigation of the Minimum Width of a Neural Network
A Numerical Investigation of the Minimum Width of a Neural Network
Ibrohim Nosirov
J. Hokanson
46
0
0
25 Oct 2019
On the space-time expressivity of ResNets
On the space-time expressivity of ResNets
J. Muller
AI4TS
217
5
0
21 Oct 2019
Hidden Unit Specialization in Layered Neural Networks: ReLU vs.
  Sigmoidal Activation
Hidden Unit Specialization in Layered Neural Networks: ReLU vs. Sigmoidal Activation
Elisa Oostwal
Michiel Straat
Michael Biehl
MLT
252
66
0
16 Oct 2019
Two-sample Testing Using Deep Learning
Two-sample Testing Using Deep LearningInternational Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Matthias Kirchler
S. Khorasani
Matthias Kirchler
Christoph Lippert
286
46
0
14 Oct 2019
DeepONet: Learning nonlinear operators for identifying differential
  equations based on the universal approximation theorem of operators
DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operatorsNature Machine Intelligence (NMI), 2019
Lu Lu
Pengzhan Jin
George Karniadakis
1.0K
2,982
0
08 Oct 2019
Online Trajectory Planning Through Combined Trajectory Optimization and
  Function Approximation: Application to the Exoskeleton Atalante
Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton AtalanteIEEE International Conference on Robotics and Automation (ICRA), 2019
Alexis Duburcq
Y. Chevaleyre
Nicolas Bredèche
Guilhem Boéris
245
14
0
01 Oct 2019
Learning Algebraic Models of Quantum Entanglement
Learning Algebraic Models of Quantum EntanglementQuantum Information Processing (QIP), 2019
Hamza Jaffali
Luke Oeding
184
10
0
27 Aug 2019
Training Optimus Prime, M.D.: Generating Medical Certification Items by
  Fine-Tuning OpenAI's gpt2 Transformer Model
Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model
M. Davier
MedImLM&MA
97
15
0
23 Aug 2019
On Object Symmetries and 6D Pose Estimation from Images
On Object Symmetries and 6D Pose Estimation from ImagesInternational Conference on 3D Vision (3DV), 2019
Giorgia Pitteri
Michael Ramamonjisoa
Slobodan Ilic
Vincent Lepetit
204
58
0
20 Aug 2019
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU
  Networks : Function Approximation and Statistical Recovery
Nonparametric Regression on Low-Dimensional Manifolds using Deep ReLU Networks : Function Approximation and Statistical Recovery
Minshuo Chen
Haoming Jiang
Wenjing Liao
T. Zhao
661
101
0
05 Aug 2019
Invariance-inducing regularization using worst-case transformations
  suffices to boost accuracy and spatial robustness
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustnessNeural Information Processing Systems (NeurIPS), 2019
Fanny Yang
Zuowen Wang
C. Heinze-Deml
287
46
0
26 Jun 2019
A Review on Deep Learning in Medical Image Reconstruction
A Review on Deep Learning in Medical Image ReconstructionJournal of the Operations Research Society of China (JORSC), 2019
Hai-Miao Zhang
Bin Dong
MedIm
384
149
0
23 Jun 2019
Neural Networks on Groups
Neural Networks on Groups
Stella Biderman
111
1
0
13 Jun 2019
Deep ReLU Networks Have Surprisingly Few Activation Patterns
Deep ReLU Networks Have Surprisingly Few Activation PatternsNeural Information Processing Systems (NeurIPS), 2019
Boris Hanin
David Rolnick
454
248
0
03 Jun 2019
Continual learning with hypernetworks
Continual learning with hypernetworksInternational Conference on Learning Representations (ICLR), 2019
J. Oswald
Christian Henning
Benjamin Grewe
João Sacramento
CLL
427
392
0
03 Jun 2019
A Review of Deep Learning with Special Emphasis on Architectures,
  Applications and Recent Trends
A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent TrendsKnowledge-Based Systems (KBS), 2019
Saptarshi Sengupta
Sanchita Basak
P. Saikia
Sayak Paul
Vasilios Tsalavoutis
Frederick Ditliac Atiah
V. Ravi
R. Peters
AI4CE
465
386
0
30 May 2019
Function approximation by deep networks
Function approximation by deep networksCommunications on Pure and Applied Analysis (CPAA), 2019
H. Mhaskar
T. Poggio
191
25
0
30 May 2019
Fully Hyperbolic Convolutional Neural Networks
Fully Hyperbolic Convolutional Neural NetworksResearch in the Mathematical Sciences (RMS), 2019
Keegan Lensink
Bas Peters
E. Haber
MedIm
216
24
0
24 May 2019
Robust learning with implicit residual networks
Robust learning with implicit residual networksMachine Learning and Knowledge Extraction (MLKE), 2019
Viktor Reshniak
Clayton Webster
OOD
320
23
0
24 May 2019
Data driven approximation of parametrized PDEs by Reduced Basis and
  Neural Networks
Data driven approximation of parametrized PDEs by Reduced Basis and Neural Networks
N. D. Santo
S. Deparis
Luca Pegolotti
202
73
0
02 Apr 2019
Generalization Error Bounds of Gradient Descent for Learning
  Over-parameterized Deep ReLU Networks
Generalization Error Bounds of Gradient Descent for Learning Over-parameterized Deep ReLU Networks
Yuan Cao
Quanquan Gu
ODLMLTAI4CE
614
166
0
04 Feb 2019
Understanding and Training Deep Diagonal Circulant Neural Networks
Understanding and Training Deep Diagonal Circulant Neural Networks
Alexandre Araujo
Benjamin Négrevergne
Y. Chevaleyre
Jamal Atif
248
5
0
29 Jan 2019
Realizing data features by deep nets
Realizing data features by deep nets
Zheng-Chu Guo
Lei Shi
Shao-Bo Lin
124
21
0
01 Jan 2019
Wireless Network Intelligence at the Edge
Wireless Network Intelligence at the Edge
Jihong Park
S. Samarakoon
M. Bennis
Mérouane Debbah
297
559
0
07 Dec 2018
Enhanced Expressive Power and Fast Training of Neural Networks by Random
  Projections
Enhanced Expressive Power and Fast Training of Neural Networks by Random ProjectionsCSIAM Transactions on Applied Mathematics (TCAM), 2018
Jian-Feng Cai
Dong Li
Jiaze Sun
Ke Wang
128
6
0
22 Nov 2018
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU
  Networks
Stochastic Gradient Descent Optimizes Over-parameterized Deep ReLU Networks
Difan Zou
Yuan Cao
Dongruo Zhou
Quanquan Gu
ODL
547
452
0
21 Nov 2018
Strong mixed-integer programming formulations for trained neural
  networks
Strong mixed-integer programming formulations for trained neural networksMathematical programming (Math. Program.), 2018
Ross Anderson
Joey Huchette
Christian Tjandraatmadja
J. Vielma
400
287
0
20 Nov 2018
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