arXiv (OAI Expanded)
open access
Dual Randomized Smoothing: Beyond Global Noise Variance
Randomized Smoothing (RS) is a prominent technique for certifying the robustness of neural networks against adversarial perturbations. With RS, achieving high accuracy at small radii requires a small noise variance, while achieving high accuracy at large radii requires a large noise variance. Howeve
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
- AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation#1143556
- Adversarial Robustness by Combining Prototype Models with Lipschitz Training#965638
- Residual-Guided Randomized Neural Networks#792288
- Residual-Guided Randomized Neural Networks#788288
- Beyond the universal law of robustness: Sharper laws for random features and neural tangent kernels#1158453
Record · ID 1122974
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