Research map / Optimization / Implicit bias and generalization
Generalization Error Bounds of Gradient Descent for Learning Over-Parameterized Deep 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
- Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks (NeurIPS 2019 · similarity 0.93)
- Generalization Error of Graph Neural Networks in the Mean-field Regime (ICML 2024 · similarity 0.91)
- Optimal Rates for Generalization of Gradient Descent for Deep ReLU Classification (NeurIPS 2025 · similarity 0.91)
- Fantastic Generalization Measures are Nowhere to be Found (ICLR 2024 · similarity 0.90)
- Simplicity Bias and Optimization Threshold in Two-Layer ReLU Networks (ICML 2025 · similarity 0.89)
- Stable Minima Cannot Overfit in Univariate ReLU Networks: Generalization by Large Step Sizes (NeurIPS 2024 · similarity 0.89)
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.