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Limitations on approximation by deep and shallow neural networks

Limitations on approximation by deep and shallow neural networks

Journal of machine learning research (JMLR), 2022
30 November 2022
G. Petrova
P. Wojtaszczyk
ArXiv (abs)PDFHTML

Papers citing "Limitations on approximation by deep and shallow neural networks"

3 / 3 papers shown
Title
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
Sharp Generalization Bounds for Foundation Models with Asymmetric Randomized Low-Rank Adapters
Anastasis Kratsios
Tin Sum Cheng
Aurelien Lucchi
Haitz Sáez de Ocáriz Borde
198
1
0
17 Jun 2025
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
301
0
0
22 May 2024
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Designing Universal Causal Deep Learning Models: The Case of Infinite-Dimensional Dynamical Systems from Stochastic Analysis
Luca Galimberti
Anastasis Kratsios
Giulia Livieri
OOD
344
19
0
24 Oct 2022
1