Deep Neural Network Approximation Theory

8 January 2019

Papers citing "Deep Neural Network Approximation Theory"

50 / 105 papers shown

Title
Super-fast rates of convergence for Neural Networks Classifiers under the Hard Margin Condition Nathanael Tepakbong Ding-Xuan Zhou Xiang Zhou 46 0 0 13 May 2025
Deep Sturm--Liouville: From Sample-Based to 1D Regularization with Learnable Orthogonal Basis Functions David Vigouroux Joseba Dalmau Louis Bethune Victor Boutin 26 0 0 09 Apr 2025
Nonlocal techniques for the analysis of deep ReLU neural network approximations Cornelia Schneider Mario Ullrich Jan Vybiral 18 0 0 07 Apr 2025
Feature Qualification by Deep Nets: A Constructive Approach Feilong Cao Shao-Bo Lin MLT 34 0 0 24 Mar 2025
Theory-to-Practice Gap for Neural Networks and Neural Operators Philipp Grohs S. Lanthaler Margaret Trautner 46 1 0 23 Mar 2025
Orthogonal Representation Learning for Estimating Causal Quantities Valentyn Melnychuk Dennis Frauen Jonas Schweisthal Stefan Feuerriegel CML OOD BDL 61 2 0 06 Feb 2025
Can neural operators always be continuously discretized? Takashi Furuya Michael Puthawala Maarten V. de Hoop Matti Lassas 66 0 0 04 Dec 2024
3D UAV Trajectory Planning for IoT Data Collection via Matrix-Based Evolutionary Computation Pei-Fa Sun Yujae Song Kang-Yu Gao Yu-Kai Wang Changjun Zhou Sang-Woon Jeon Jun Zhang 19 0 0 08 Oct 2024
Deep non-parametric logistic model with case-control data and external summary information Hengchao Shi M. Zheng Wen Yu 35 0 0 03 Sep 2024
Solving The Quantum Many-Body Hamiltonian Learning Problem with Neural Differential Equations Timothy Heightman Edward Jiang Antonio Acín AI4CE 51 4 0 16 Aug 2024
Computability of Classification and Deep Learning: From Theoretical Limits to Practical Feasibility through Quantization Holger Boche Vít Fojtík Adalbert Fono Gitta Kutyniok 35 0 0 12 Aug 2024
Operator Learning of Lipschitz Operators: An Information-Theoretic Perspective Samuel Lanthaler 47 3 0 26 Jun 2024
On the growth of the parameters of approximating ReLU neural networks Erion Morina Martin Holler 37 0 0 21 Jun 2024
Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation Madison Cooley Shandian Zhe Robert M. Kirby Varun Shankar 70 1 0 04 Jun 2024
Physics-informed deep learning and compressive collocation for high-dimensional diffusion-reaction equations: practical existence theory and numerics Simone Brugiapaglia N. Dexter Samir Karam Weiqi Wang AI4CE DiffM 43 1 0 03 Jun 2024
Approximation Error and Complexity Bounds for ReLU Networks on Low-Regular Function Spaces Owen Davis Gianluca Geraci Mohammad Motamed 46 2 0 10 May 2024
Pragmatist Intelligence: Where the Principle of Usefulness Can Take ANNs Antonio Bikić Sayan Mukherjee 20 0 0 07 May 2024
Understanding the Difficulty of Solving Cauchy Problems with PINNs Tao Wang Bo Zhao Sicun Gao Rose Yu 49 1 0 04 May 2024
Learning smooth functions in high dimensions: from sparse polynomials to deep neural networks Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 42 4 0 04 Apr 2024
A practical existence theorem for reduced order models based on convolutional autoencoders N. R. Franco Simone Brugiapaglia AI4CE 33 4 0 01 Feb 2024
Deep Neural Networks: A Formulation Via Non-Archimedean Analysis W. A. Zúniga-Galindo AI4CE 14 1 0 31 Jan 2024
Extracting Formulae in Many-Valued Logic from Deep Neural Networks Yani Zhang Helmut Bölcskei 24 0 0 22 Jan 2024
Do stable neural networks exist for classification problems? -- A new view on stability in AI Z. N. D. Liu A. C. Hansen 30 0 0 15 Jan 2024
Universal Consistency of Wide and Deep ReLU Neural Networks and Minimax Optimal Convergence Rates for Kolmogorov-Donoho Optimal Function Classes Hyunouk Ko Xiaoming Huo 42 1 0 08 Jan 2024
Sampling Complexity of Deep Approximation Spaces Ahmed Abdeljawad Philipp Grohs 26 1 0 20 Dec 2023
Deep State-Space Model for Predicting Cryptocurrency Price Shalini Sharma A. Majumdar Émilie Chouzenoux Victor Elvira 28 0 0 21 Nov 2023
Neural Snowflakes: Universal Latent Graph Inference via Trainable Latent Geometries Haitz Sáez de Ocáriz Borde Anastasis Kratsios 42 4 0 23 Oct 2023
Residual Multi-Fidelity Neural Network Computing Owen Davis Mohammad Motamed Raúl Tempone 37 1 0 05 Oct 2023
On Excess Risk Convergence Rates of Neural Network Classifiers Hyunouk Ko Namjoon Suh X. Huo 21 2 0 26 Sep 2023
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 40 1 0 13 Sep 2023
Approximation Results for Gradient Descent trained Neural Networks G. Welper 48 0 0 09 Sep 2023
$A numerical approach for the fractional Laplacian via deep neural networks$ A numerical approach for the fractional Laplacian via deep neural networks Nicolás Valenzuela 34 3 0 30 Aug 2023
What and How does In-Context Learning Learn? Bayesian Model Averaging, Parameterization, and Generalization Yufeng Zhang Fengzhuo Zhang Zhuoran Yang Zhaoran Wang BDL 36 65 0 30 May 2023
Error Analysis of Physics-Informed Neural Networks for Approximating Dynamic PDEs of Second Order in Time Y. Qian Yongchao Zhang Yuanfei Huang S. Dong PINN 21 1 0 22 Mar 2023
Topological Learning in Multi-Class Data Sets Christopher H. Griffin Trevor K. Karn Benjamin Apple AI4CE 28 0 0 23 Jan 2023
Approaching Globally Optimal Energy Efficiency in Interference Networks via Machine Learning Bile Peng Karl-Ludwig Besser Ramprasad Raghunath Eduard Axel Jorswieck 16 2 0 25 Nov 2022
Universal Time-Uniform Trajectory Approximation for Random Dynamical Systems with Recurrent Neural Networks A. Bishop 42 1 0 15 Nov 2022
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis Luca Galimberti Anastasis Kratsios Giulia Livieri OOD 28 14 0 24 Oct 2022
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities S. Lanthaler Roberto Molinaro Patrik Hadorn Siddhartha Mishra 59 24 0 03 Oct 2022
Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$ R. Gentile G. Welper ODL 56 6 0 17 Sep 2022
Error Controlled Feature Selection for Ultrahigh Dimensional and Highly Correlated Feature Space Using Deep Learning Arkaprabha Ganguli D. Todem T. Maiti OOD 23 0 0 15 Sep 2022
From Monte Carlo to neural networks approximations of boundary value problems L. Beznea Iulian Cîmpean Oana Lupascu-Stamate Ionel Popescu A. Zarnescu 22 1 0 03 Sep 2022
Stochastic Scaling in Loss Functions for Physics-Informed Neural Networks Ethan A Mills Alexey Pozdnyakov 16 1 0 07 Aug 2022
The BUTTER Zone: An Empirical Study of Training Dynamics in Fully Connected Neural Networks Charles Edison Tripp J. Perr-Sauer L. Hayne M. Lunacek Jamil Gafur AI4CE 28 1 0 25 Jul 2022
Approximation Power of Deep Neural Networks: an explanatory mathematical survey Mohammad Motamed 12 3 0 19 Jul 2022
Error analysis for deep neural network approximations of parametric hyperbolic conservation laws Tim De Ryck Siddhartha Mishra PINN 15 10 0 15 Jul 2022
Adaptive deep learning for nonlinear time series models Daisuke Kurisu Riku Fukami Yuta Koike AI4TS 16 5 0 06 Jul 2022
Data-driven reduced order models using invariant foliations, manifolds and autoencoders R. Szalai AI4CE 14 9 0 24 Jun 2022
Compressive Fourier collocation methods for high-dimensional diffusion equations with periodic boundary conditions Weiqi Wang Simone Brugiapaglia 27 2 0 02 Jun 2022
Learning ReLU networks to high uniform accuracy is intractable Julius Berner Philipp Grohs F. Voigtlaender 32 4 0 26 May 2022