Research map / Learning theory and algorithms / Sample complexity
Universal approximation power of deep residual neural networks via nonlinear control theory
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Closest papers by meaning
- A closer look at the approximation capabilities of neural networks (ICLR 2020 · similarity 0.90)
- Non-Euclidean Universal Approximation (NeurIPS 2020 · similarity 0.89)
- Characterizing ResNet's Universal Approximation Capability (ICML 2024 · similarity 0.87)
- Universal Approximation Theorem of Deep Q-Networks (ICML 2025 · similarity 0.86)
- ReLU Network with Width $d+\mathcal{O}(1)$ Can Achieve Optimal Approximation Rate (ICML 2024 · similarity 0.86)
- Implicit regularization of deep residual networks towards neural ODEs (ICLR 2024 · similarity 0.85)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.