Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective

21 May 2020

Papers citing "Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective"

22 / 22 papers shown

Title
Curse of Dimensionality in Neural Network Optimization Sanghoon Na Haizhao Yang 51 0 0 07 Feb 2025
Sinc Kolmogorov-Arnold Network and Its Applications on Physics-informed Neural Networks Tianchi Yu Jingwei Qiu Jiang Yang Ivan V. Oseledets 23 2 0 05 Oct 2024
Fourier Spectral Physics Informed Neural Network: An Efficient and Low-Memory PINN Tianchi Yu Yiming Qi Ivan V. Oseledets Shiyi Chen 24 0 0 29 Aug 2024
Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks Gyu Seon Kim Yeryeong Cho Jaehyun Chung Soohyun Park Soyi Jung Zhu Han Joongheon Kim 28 2 0 24 Jun 2024
Learning with Norm Constrained, Over-parameterized, Two-layer Neural Networks Fanghui Liu L. Dadi V. Cevher 68 2 0 29 Apr 2024
Sampling weights of deep neural networks Erik Lien Bolager Iryna Burak Chinmay Datar Q. Sun Felix Dietrich BDL UQCV 19 16 0 29 Jun 2023
Can Physics-Informed Neural Networks beat the Finite Element Method? T. G. Grossmann U. J. Komorowska J. Latz Carola-Bibiane Schönlieb PINN AI4CE 13 85 0 08 Feb 2023
Evolution of MAC Protocols in the Machine Learning Decade: A Comprehensive Survey Mostafa Hussien I. Taj-Eddin Mohammed F. A. Ahmed Ali Ranjha K. Nguyen M. Cheriet AI4TS 10 8 0 24 Jan 2023
On adversarial robustness and the use of Wasserstein ascent-descent dynamics to enforce it Camilo A. Garcia Trillos Nicolas García Trillos 16 5 0 09 Jan 2023
A Functional-Space Mean-Field Theory of Partially-Trained Three-Layer Neural Networks Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 10 5 0 28 Oct 2022
Proximal Mean Field Learning in Shallow Neural Networks Alexis M. H. Teter Iman Nodozi A. Halder FedML 35 1 0 25 Oct 2022
Intrinsic Dimension for Large-Scale Geometric Learning Maximilian Stubbemann Tom Hanika Friedrich Martin Schneider PINN 14 5 0 11 Oct 2022
Lagrangian PINNs: A causality-conforming solution to failure modes of physics-informed neural networks R. Mojgani Maciej Balajewicz P. Hassanzadeh PINN 25 45 0 05 May 2022
On Feature Learning in Neural Networks with Global Convergence Guarantees Zhengdao Chen Eric Vanden-Eijnden Joan Bruna MLT 20 12 0 22 Apr 2022
A Neurorobotics Approach to Behaviour Selection based on Human Activity Recognition C. M. Ranieri R. Moioli P. A. Vargas R. Romero 14 0 0 27 Jul 2021
Learning the solution operator of parametric partial differential equations with physics-informed DeepOnets Sifan Wang Hanwen Wang P. Perdikaris AI4CE 38 661 0 19 Mar 2021
Towards a Mathematical Understanding of Neural Network-Based Machine Learning: what we know and what we don't E. Weinan Chao Ma Stephan Wojtowytsch Lei Wu AI4CE 6 133 0 22 Sep 2020
On the Banach spaces associated with multi-layer ReLU networks: Function representation, approximation theory and gradient descent dynamics E. Weinan Stephan Wojtowytsch MLT 13 53 0 30 Jul 2020
The Quenching-Activation Behavior of the Gradient Descent Dynamics for Two-layer Neural Network Models Chao Ma Lei Wu E. Weinan MLT 13 10 0 25 Jun 2020
Representation formulas and pointwise properties for Barron functions E. Weinan Stephan Wojtowytsch 18 79 0 10 Jun 2020
On the Convergence of Gradient Descent Training for Two-layer ReLU-networks in the Mean Field Regime Stephan Wojtowytsch MLT 16 49 0 27 May 2020
Kolmogorov Width Decay and Poor Approximators in Machine Learning: Shallow Neural Networks, Random Feature Models and Neural Tangent Kernels E. Weinan Stephan Wojtowytsch 26 31 0 21 May 2020