ICLR 2026 - Submissions
Submissions
Summary Statistics
| Quantity AI Content | Count | Avg Rating |
|---|---|---|
| 0-10% | 1 (100%) | 4.67 |
| 10-30% | 0 (0%) | N/A |
| 30-50% | 0 (0%) | N/A |
| 50-70% | 0 (0%) | N/A |
| 70-90% | 0 (0%) | N/A |
| 90-100% | 0 (0%) | N/A |
| Total | 1 (100%) | 4.67 |
| Title | Abstract | Avg Rating | Quantity AI Content | Reviews | Pangram Dashboard |
|---|---|---|---|---|---|
| Quantifying Statistical Significance in Diffusion-Based Anomaly Localization via Selective Inference | Anomaly localization in images—identifying regions that deviate from expected patterns—is vital in applications such as medical diagnosis and industrial inspection. A recent trend is the use of image ... | 4.67 | 0% | See Reviews | View AI Dashboard |