The gap between theory and practice in function approximation with deep neural networks

16 January 2020

Papers citing "The gap between theory and practice in function approximation with deep neural networks"

16 / 16 papers shown

Title
An incremental algorithm for non-convex AI-enhanced medical image processing Elena Morotti 34 0 0 13 May 2025
Enabling Local Neural Operators to perform Equation-Free System-Level Analysis Gianluca Fabiani H. Vandecasteele S. Goswami Constantinos Siettos Ioannis G. Kevrekidis 132 0 0 05 May 2025
Statistical Mechanics and Artificial Neural Networks: Principles, Models, and Applications Lucas Böttcher Gregory R. Wheeler 32 0 0 05 Apr 2024
Mathematical Algorithm Design for Deep Learning under Societal and Judicial Constraints: The Algorithmic Transparency Requirement Holger Boche Adalbert Fono Gitta Kutyniok FaML 31 4 0 18 Jan 2024
Fundamental Limits of Deep Learning-Based Binary Classifiers Trained with Hinge Loss T. Getu Georges Kaddoum M. Bennis 37 1 0 13 Sep 2023
To be or not to be stable, that is the question: understanding neural networks for inverse problems David Evangelista J. Nagy E. Morotti E. L. Piccolomini 28 4 0 24 Nov 2022
Parameter-varying neural ordinary differential equations with partition-of-unity networks Kookjin Lee N. Trask 22 2 0 01 Oct 2022
Approximation results for Gradient Descent trained Shallow Neural Networks in $1d$ R. Gentile G. Welper ODL 52 6 0 17 Sep 2022
Solving Elliptic Problems with Singular Sources using Singularity Splitting Deep Ritz Method Tianhao Hu Bangti Jin Zhi Zhou 23 6 0 07 Sep 2022
Sparse Deep Neural Network for Nonlinear Partial Differential Equations Yuesheng Xu T. Zeng 30 5 0 27 Jul 2022
Learning ReLU networks to high uniform accuracy is intractable Julius Berner Philipp Grohs F. Voigtlaender 32 4 0 26 May 2022
Limitations of Deep Learning for Inverse Problems on Digital Hardware Holger Boche Adalbert Fono Gitta Kutyniok 24 25 0 28 Feb 2022
The mathematics of adversarial attacks in AI -- Why deep learning is unstable despite the existence of stable neural networks Alexander Bastounis A. Hansen Verner Vlacic AAML OOD 24 28 0 13 Sep 2021
Proof of the Theory-to-Practice Gap in Deep Learning via Sampling Complexity bounds for Neural Network Approximation Spaces Philipp Grohs F. Voigtlaender 8 34 0 06 Apr 2021
Partition of unity networks: deep hp-approximation Kookjin Lee N. Trask Ravi G. Patel Mamikon A. Gulian E. Cyr 14 30 0 27 Jan 2021
Deep Neural Networks Are Effective At Learning High-Dimensional Hilbert-Valued Functions From Limited Data Ben Adcock Simone Brugiapaglia N. Dexter S. Moraga 34 29 0 11 Dec 2020