Research map / Graph learning / Graph algorithms and clustering
Non-Uniform Subset Selection for Active Learning in Structured Data
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use graph relationships and joint-entropy subset selection for active learning | SVM recognition on SUN scene classification · SUN | manual labeling required to reach ALL: 41% | Helped |
| Use graph relationships and joint-entropy subset selection for active learning | SVM recognition on SUN object classification · SUN | manual labeling required to reach ALL: 62% | Helped |
| Use graph relationships and joint-entropy subset selection for active learning | SVM document classification · CORA | manual labeling required to reach ALL: 42% | Helped |
| Use graph relationships and joint-entropy subset selection for active learning | SVM activity classification · VIRAT | manual labeling required to reach ALL accuracy: 18% | Helped |
| Use alpha=0.5 rather than alpha=1.1 (ablation) | Proposed active learning method · CORA | manual labeling required for similar final performance | Hurt |
| Use alpha=1.1 rather than alpha=0.5 (ablation) | Proposed active learning method · CORA | final accuracy | No clear effect |
Closest papers by meaning
- Contextual Active Model Selection (NeurIPS 2024 · similarity 0.85)
- Towards Cost-Effective Learning: A Synergy of Semi-Supervised and Active Learning (CVPR 2025 · similarity 0.84)
- Structural-Entropy-Based Sample Selection for Efficient and Effective Learning (ICLR 2025 · similarity 0.84)
- Towards a statistical theory of data selection under weak supervision (ICLR 2024 · similarity 0.84)
- A Cross-Domain Benchmark for Active Learning (NeurIPS 2024 · similarity 0.84)
- Robust Offline Active Learning on Graphs (NeurIPS 2024 · similarity 0.84)
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