Research map / Optimization / Implicit bias and generalization
A single gradient step finds adversarial examples on random two-layers neural 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
- Wide Two-Layer Networks can Learn from Adversarial Perturbations (NeurIPS 2024 · similarity 0.86)
- Asymptotics of feature learning in two-layer networks after one gradient-step (ICML 2024 · similarity 0.85)
- Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure (ICLR 2025 · similarity 0.85)
- Adversarial generalization of unfolding (model-based) networks (NeurIPS 2025 · similarity 0.85)
- Learning a Single Neuron Robustly to Distributional Shifts and Adversarial Label Noise (NeurIPS 2024 · similarity 0.85)
- A Sublinear Adversarial Training Algorithm (ICLR 2024 · similarity 0.85)
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.