Research map / Learning theory and algorithms / Sample complexity
Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space
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
- AdaGrad under Anisotropic Smoothness (ICLR 2025 · similarity 0.88)
- Minimax Adaptive Online Nonparametric Regression over Besov spaces (NeurIPS 2025 · similarity 0.85)
- Posterior Contraction for Sparse Neural Networks in Besov Spaces with Intrinsic Dimensionality (NeurIPS 2025 · similarity 0.85)
- High-Probability Minimax Adaptive Estimation in Besov Spaces via Online-to-Batch (NeurIPS 2026 · similarity 0.84)
- Posterior Contraction Rates for sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces (NeurIPS 2026 · similarity 0.84)
- Stable Minima of ReLU Neural Networks Suffer from the Curse of Dimensionality: The Neural Shattering Phenomenon (NeurIPS 2025 · similarity 0.83)
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.
