arXiv (OAI)
open access
Two-Dimensional Deep ReLU CNN Approximation for Korobov Functions: A Constructive Approach
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Minimax rates for learning spectral Barron functions by deep ReLU neural networks#1143987
- Minimal-Norm Univariate Two-Layer ReLU Classification: Exact Solutions and Global Optimality with Skip Connections#1065041
- Riemannian Deep Learning:Modules, Networks, and Geometries#386858
- A Controlled Benchmark of Quantum-Latent GAN Augmentation for Brain MRI#287184
- Building Better Encoder-only Cross-Encoders: A Controlled Study of Training Strategies for Neural Re-ranking#792712
Record · ID 149233
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive