Research map / Adversarial robustness and security / Certified and empirical robustness
Adversarial Examples in Multi-Layer Random ReLU Networks
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
- Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations (NeurIPS 2020 · similarity 0.92)
- A single gradient step finds adversarial examples on random two-layers neural networks (NeurIPS 2021 · similarity 0.91)
- Adversarial Robustness Guarantees for Random Deep Neural Networks (ICML 2021 · similarity 0.90)
- Adversarial Examples Exist in Two-Layer ReLU Networks for Low Dimensional Linear Subspaces (NeurIPS 2023 · similarity 0.89)
- Wide Two-Layer Networks can Learn from Adversarial Perturbations (NeurIPS 2024 · similarity 0.89)
- Adversarial Training Can Provably Improve Robustness: Theoretical Analysis of Feature Learning Process Under Structured Data (ICLR 2025 · similarity 0.86)
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.
