Research map / Adversarial robustness and security / Certified and empirical robustness
The Double-Edged Sword of Implicit Bias: Generalization vs. Robustness in 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
- Can Implicit Bias Imply Adversarial Robustness? (ICML 2024 · similarity 0.94)
- Gradient Methods Provably Converge to Non-Robust Networks (NeurIPS 2022 · similarity 0.93)
- The Implicit Bias of Gradient Descent toward Collaboration between Layers: A Dynamic Analysis of Multilayer Perceptions (NeurIPS 2024 · similarity 0.91)
- The Price of Implicit Bias in Adversarially Robust Generalization (NeurIPS 2024 · similarity 0.90)
- Feature Averaging: An Implicit Bias of Gradient Descent Leading to Non-Robustness in Neural Networks (ICLR 2025 · similarity 0.90)
- Simplicity Bias and Optimization Threshold in Two-Layer ReLU Networks (ICML 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.
