Research map / Optimization / Implicit bias and generalization
Uniform convergence may be unable to explain generalization in deep learning
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.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| increase training set size m | norm-based generalization bounds (Neyshabur+'18, Bartlett+'17) for 5-layer MLP, SGD batch size 1 · MNIST | generalization bound | Hurt |
| increase training set size m | 5-layer MLP trained by SGD · MNIST | test error | Helped |
| use larger batch size | norm-based generalization bounds · MNIST | bound dependence on m | Helped |
Closest papers by meaning
- Fantastic Generalization Measures are Nowhere to be Found (ICLR 2024 · similarity 0.93)
- Simplicity Bias in Overparameterized Machine Learning (AAAI 2024 · similarity 0.90)
- Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study (NeurIPS 2025 · similarity 0.90)
- Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Power (ICLR 2025 · similarity 0.89)
- Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks (ICML 2024 · similarity 0.89)
- How Uniform Random Weights Induce Non-uniform Bias: Typical Interpolating Neural Networks Generalize with Narrow Teachers (ICML 2024 · 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.
