Research map / Adversarial robustness and security / Certified and empirical robustness
Adversarial Robustness through Random Weight Sampling
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
- Adversarial Robustness via Deformable Convolution with Stochasticity (ICML 2025 · similarity 0.93)
- Theoretical evidence for adversarial robustness through randomization (NeurIPS 2019 · similarity 0.91)
- Are Adversarial Examples Created Equal? A Learnable Weighted Minimax Risk for Robustness under Non-uniform Attacks (AAAI 2021 · similarity 0.91)
- Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks (ICLR 2024 · similarity 0.90)
- Randomized Adversarial Training via Taylor Expansion (CVPR 2023 · similarity 0.90)
- Efficient Robustness Evaluation via Constraint Relaxation (AAAI 2025 · similarity 0.88)
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.