Research map / Label-efficient and robust learning / Noisy labels
Modern Neural Networks Generalize on Small Data Sets
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| random forest instead of unregularized 10-layer NN | UCI classification · 116 UCI datasets | accuracy: 2.4% | Helped |
| add dropout (keep 0.85) | 10x100 NN · 116 UCI datasets | accuracy | No clear effect |
| ensemble of decorrelated sub-networks vs single sub-network | 10x100 NN · 2D circle simulation | test accuracy: 79%-82% -> 85% | Helped |
Closest papers by meaning
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- Rethinking Benign Overfitting in Two-Layer Neural Networks (ICML 2025 · similarity 0.86)
- MaxSup: Overcoming Representation Collapse in Label Smoothing (NeurIPS 2025 · similarity 0.86)
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