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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"

50 / 633 papers shown
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments
  and Observational Data
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
Miruna Oprescu
Nathan Kallus
CML
233
1
0
10 Jun 2024
A Low Rank Neural Representation of Entropy Solutions
A Low Rank Neural Representation of Entropy Solutions
Donsub Rim
Gerrit Welper
323
1
0
09 Jun 2024
Deep Neural Networks are Adaptive to Function Regularity and Data
  Distribution in Approximation and Estimation
Deep Neural Networks are Adaptive to Function Regularity and Data Distribution in Approximation and Estimation
Hao Liu
Jiahui Cheng
Wenjing Liao
178
1
0
08 Jun 2024
Posterior and variational inference for deep neural networks with heavy-tailed weights
Posterior and variational inference for deep neural networks with heavy-tailed weights
Ismael Castillo
Paul Egels
BDL
336
10
0
05 Jun 2024
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
Ocheme Anthony Ekle
William Eberle
AI4TS
327
39
0
31 May 2024
From Words to Actions: Unveiling the Theoretical Underpinnings of
  LLM-Driven Autonomous Systems
From Words to Actions: Unveiling the Theoretical Underpinnings of LLM-Driven Autonomous Systems
Jianliang He
Siyu Chen
Fengzhuo Zhang
Zhuoran Yang
LM&RoLLMAG
303
8
0
30 May 2024
Lower Bounds on the Expressivity of Recurrent Neural Language Models
Lower Bounds on the Expressivity of Recurrent Neural Language Models
Anej Svete
Franz Nowak
Anisha Mohamed Sahabdeen
Robert Bamler
265
0
0
29 May 2024
How many samples are needed to train a deep neural network?
How many samples are needed to train a deep neural network?
Pegah Golestaneh
Mahsa Taheri
Johannes Lederer
242
7
0
26 May 2024
Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines
Deep Ridgelet Transform and Unified Universality Theorem for Deep and Shallow Joint-Group-Equivariant Machines
Sho Sonoda
Yuka Hashimoto
Isao Ishikawa
Masahiro Ikeda
353
0
0
22 May 2024
Model Free Prediction with Uncertainty Assessment
Model Free Prediction with Uncertainty Assessment
Yuling Jiao
Lican Kang
Jin Liu
Heng Peng
Heng Zuo
DiffM
357
2
0
21 May 2024
Approximation and Gradient Descent Training with Neural Networks
Approximation and Gradient Descent Training with Neural Networks
G. Welper
232
2
0
19 May 2024
Geometry-Aware Instrumental Variable Regression
Geometry-Aware Instrumental Variable RegressionInternational Conference on Machine Learning (ICML), 2024
Heiner Kremer
Bernhard Schölkopf
286
0
0
19 May 2024
Error Analysis of Three-Layer Neural Network Trained with PGD for Deep
  Ritz Method
Error Analysis of Three-Layer Neural Network Trained with PGD for Deep Ritz MethodIEEE Transactions on Information Theory (IEEE Trans. Inf. Theory), 2024
Yuling Jiao
Yanming Lai
Yang Wang
AI4CE
177
1
0
19 May 2024
Approximation Error and Complexity Bounds for ReLU Networks on
  Low-Regular Function Spaces
Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces
Owen Davis
Gianluca Geraci
Mohammad Motamed
185
2
0
10 May 2024
Generalization analysis with deep ReLU networks for metric and
  similarity learning
Generalization analysis with deep ReLU networks for metric and similarity learning
Junyu Zhou
Puyu Wang
Ding-Xuan Zhou
212
3
0
10 May 2024
Towards Accurate and Robust Architectures via Neural Architecture Search
Towards Accurate and Robust Architectures via Neural Architecture SearchComputer Vision and Pattern Recognition (CVPR), 2024
Yuwei Ou
Yuqi Feng
Yanan Sun
AAML
196
9
0
09 May 2024
Generative adversarial learning with optimal input dimension and its
  adaptive generator architecture
Generative adversarial learning with optimal input dimension and its adaptive generator architecture
Zhiyao Tan
Ling Zhou
Huazhen Lin
GAN
228
2
0
06 May 2024
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural
  Networks
No One-Size-Fits-All Neurons: Task-based Neurons for Artificial Neural Networks
Feng-Lei Fan
Meng Wang
Hang Dong
Jianwei Ma
Tieyong Zeng
190
2
0
03 May 2024
KAN: Kolmogorov-Arnold Networks
KAN: Kolmogorov-Arnold Networks
Ziming Liu
Yixuan Wang
Sachin Vaidya
Fabian Ruehle
James Halverson
Marin Soljacic
Thomas Y. Hou
Max Tegmark
974
1,236
0
30 Apr 2024
Learning smooth functions in high dimensions: from sparse polynomials to
  deep neural networks
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks
Ben Adcock
Simone Brugiapaglia
N. Dexter
S. Moraga
214
8
0
04 Apr 2024
On the rates of convergence for learning with convolutional neural networks
On the rates of convergence for learning with convolutional neural networks
Yunfei Yang
Han Feng
Ding-Xuan Zhou
406
4
0
25 Mar 2024
A Wasserstein perspective of Vanilla GANs
A Wasserstein perspective of Vanilla GANsNeural Networks (NN), 2024
Lea Kunkel
Mathias Trabs
184
14
0
22 Mar 2024
Adaptive Multilevel Neural Networks for Parametric PDEs with Error
  Estimation
Adaptive Multilevel Neural Networks for Parametric PDEs with Error Estimation
Janina Enrica Schutte
Martin Eigel
AI4CE
200
2
0
19 Mar 2024
Two-hidden-layer ReLU neural networks and finite elements
Two-hidden-layer ReLU neural networks and finite elements
Pengzhan Jin
309
1
0
09 Mar 2024
Applied Causal Inference Powered by ML and AI
Applied Causal Inference Powered by ML and AI
Victor Chernozhukov
Christian Hansen
Nathan Kallus
Martin Spindler
Vasilis Syrgkanis
CML
329
48
0
04 Mar 2024
Exponential Expressivity of ReLU$^k$ Neural Networks on Gevrey Classes
  with Point Singularities
Exponential Expressivity of ReLUk^kk Neural Networks on Gevrey Classes with Point Singularities
J. Opschoor
Christoph Schwab
143
5
0
04 Mar 2024
Operator Learning: Algorithms and Analysis
Operator Learning: Algorithms and Analysis
Nikola B. Kovachki
S. Lanthaler
Andrew M. Stuart
458
67
0
24 Feb 2024
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically
  Low-dimensional Data
A Statistical Analysis of Wasserstein Autoencoders for Intrinsically Low-dimensional Data
Saptarshi Chakraborty
Peter L. Bartlett
181
3
0
24 Feb 2024
On Minimal Depth in Neural Networks
On Minimal Depth in Neural Networks
J. L. Valerdi
256
10
0
23 Feb 2024
Depth Separation in Norm-Bounded Infinite-Width Neural Networks
Depth Separation in Norm-Bounded Infinite-Width Neural Networks
Suzanna Parkinson
Greg Ongie
Rebecca Willett
Ohad Shamir
Nathan Srebro
MDE
244
2
0
13 Feb 2024
Score-based generative models break the curse of dimensionality in
  learning a family of sub-Gaussian probability distributions
Score-based generative models break the curse of dimensionality in learning a family of sub-Gaussian probability distributions
Frank Cole
Yuxuan Zhao
DiffM
331
8
0
12 Feb 2024
Disparate Impact on Group Accuracy of Linearization for Private
  Inference
Disparate Impact on Group Accuracy of Linearization for Private InferenceInternational Conference on Machine Learning (ICML), 2024
Saswat Das
Marco Romanelli
Ferdinando Fioretto
FedML
218
4
0
06 Feb 2024
Partially Stochastic Infinitely Deep Bayesian Neural Networks
Partially Stochastic Infinitely Deep Bayesian Neural NetworksInternational Conference on Machine Learning (ICML), 2024
Sergio Calvo-Ordoñez
Matthieu Meunier
Francesco Piatti
Yuantao Shi
BDL
439
6
0
05 Feb 2024
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of
  Experts
Approximation Rates and VC-Dimension Bounds for (P)ReLU MLP Mixture of Experts
Anastasis Kratsios
Haitz Sáez de Ocáriz Borde
Takashi Furuya
Marc T. Law
MoE
510
2
0
05 Feb 2024
A practical existence theorem for reduced order models based on
  convolutional autoencoders
A practical existence theorem for reduced order models based on convolutional autoencoders
N. R. Franco
Simone Brugiapaglia
AI4CE
310
10
0
01 Feb 2024
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev Loss
Deeper or Wider: A Perspective from Optimal Generalization Error with Sobolev LossInternational Conference on Machine Learning (ICML), 2024
Yahong Yang
Juncai He
AI4CE
486
13
0
31 Jan 2024
At the junction between deep learning and statistics of extremes:
  formalizing the landslide hazard definition
At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definitionJournal of Geophysical Research (JGR), 2024
Ashok Dahal
Raphael Huser
Luigi Lombardo
81
16
0
25 Jan 2024
Can overfitted deep neural networks in adversarial training generalize?
  -- An approximation viewpoint
Can overfitted deep neural networks in adversarial training generalize? -- An approximation viewpoint
Zhongjie Shi
Fanghui Liu
Yuan Cao
Johan A. K. Suykens
236
0
0
24 Jan 2024
Extracting Formulae in Many-Valued Logic from Deep Neural Networks
Extracting Formulae in Many-Valued Logic from Deep Neural NetworksIEEE Transactions on Signal Processing (IEEE TSP), 2024
Yani Zhang
Helmut Bölcskei
177
0
0
22 Jan 2024
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model
  Reduction for Operator Learning
Generalization Error Guaranteed Auto-Encoder-Based Nonlinear Model Reduction for Operator Learning
Hao Liu
Biraj Dahal
Rongjie Lai
Wenjing Liao
AI4CE
231
10
0
19 Jan 2024
Approximation of Solution Operators for High-dimensional PDEs
Approximation of Solution Operators for High-dimensional PDEs
Nathan Gaby
Xiaojing Ye
236
0
0
18 Jan 2024
Mathematical Algorithm Design for Deep Learning under Societal and
  Judicial Constraints: The Algorithmic Transparency Requirement
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement
Holger Boche
Adalbert Fono
Gitta Kutyniok
FaML
351
5
0
18 Jan 2024
A Survey on Statistical Theory of Deep Learning: Approximation, Training
  Dynamics, and Generative Models
A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative ModelsAnnual Review of Statistics and Its Application (ARSIA), 2024
Namjoon Suh
Guang Cheng
MedIm
354
18
0
14 Jan 2024
Error estimation for physics-informed neural networks with implicit
  Runge-Kutta methods
Error estimation for physics-informed neural networks with implicit Runge-Kutta methods
Jochen Stiasny
Spyros Chatzivasileiadis
PINN
164
2
0
10 Jan 2024
Optimization Over Trained Neural Networks: Taking a Relaxing Walk
Optimization Over Trained Neural Networks: Taking a Relaxing Walk
Jiatai Tong
Junyang Cai
Thiago Serra
379
14
0
07 Jan 2024
Nonlinear functional regression by functional deep neural network with kernel embedding
Nonlinear functional regression by functional deep neural network with kernel embedding
Zhongjie Shi
Jun Fan
Linhao Song
Ding-Xuan Zhou
Johan A. K. Suykens
736
6
0
05 Jan 2024
Double-well Net for Image Segmentation
Double-well Net for Image SegmentationMultiscale Modeling & simulation (MMS), 2023
Haotian Liu
Jun Liu
Raymond H. F. Chan
Xue-Cheng Tai
235
10
0
31 Dec 2023
Density estimation using the perceptron
Density estimation using the perceptron
P. R. Gerber
Tianze Jiang
Yury Polyanskiy
Rui Sun
286
0
0
29 Dec 2023
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU
  Networks
Computational Tradeoffs of Optimization-Based Bound Tightening in ReLU Networks
Fabian Badilla
Marcos Goycoolea
Gonzalo Muñoz
Thiago Serra
308
8
0
27 Dec 2023
Expressivity and Approximation Properties of Deep Neural Networks with
  ReLU$^k$ Activation
Expressivity and Approximation Properties of Deep Neural Networks with ReLUk^kk Activation
Juncai He
Tong Mao
Jinchao Xu
321
7
0
27 Dec 2023
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