arXiv (All)
open access
Approximation Property of Dropout Neural Networks: Sobolev Rates and Confidence Bounds
The universal approximation property of dropout neural networks does not by itself describe the network size required for an accurate random realization. In this work, we study approximation of the unit ball of $W^{n,\infty}([0,1]^d)$ by ReLU networks whose edges are retained independently with prob
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
- Near-optimal estimates for the $\ell^p$-Lipschitz constants of deep random ReLU neural networks#997975
- Nearly Tight Rademacher Bounds for Sparsely Activated Neural Networks#986729
- Limit theorems for stochastic gradient descent in high-dimensional single-layer networks#675881
- Statistical Properties of Deep Neural Networks with Dependent Data#1063726
- On the $O(\frac{\sqrt{d}}{K^{1/4}})$ Convergence Rate of AdamW Measured by $\ell_1$ Norm#1117229
Record · ID 1186567
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