Research map / Learning theory and algorithms / Sample complexity
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds
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
- ReLU Network with Width $d+\mathcal{O}(1)$ Can Achieve Optimal Approximation Rate (ICML 2024 · similarity 0.88)
- Minimum width for universal approximation using ReLU networks on compact domain (ICLR 2024 · similarity 0.87)
- Approximating Latent Manifolds in Neural Networks via Vanishing Ideals (ICML 2025 · similarity 0.87)
- Approximation to Smooth Functions by Low-Rank Swish Networks (ICML 2025 · similarity 0.86)
- Characterizing ResNet's Universal Approximation Capability (ICML 2024 · similarity 0.86)
- Compelling ReLU Networks to Exhibit Exponentially Many Linear Regions at Initialization and During Training (ICML 2025 · similarity 0.86)
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.
