ICLR 2026 - Submissions
Submissions
Summary Statistics
| Quantity AI Content | Count | Avg Rating |
|---|---|---|
| 0-10% | 0 (0%) | N/A |
| 10-30% | 0 (0%) | N/A |
| 30-50% | 1 (100%) | 4.29 |
| 50-70% | 0 (0%) | N/A |
| 70-90% | 0 (0%) | N/A |
| 90-100% | 0 (0%) | N/A |
| Total | 1 (100%) | 4.29 |
| Title | Abstract | Avg Rating | Quantity AI Content | Reviews | Pangram Dashboard |
|---|---|---|---|---|---|
| Dissecting Mahalanobis: How Feature Geometry and Normalization Shape OOD Detection | Out-of-distribution (OOD) detection is critical for the reliable deployment and better understanding of deep learning models. To address this challenge, various methods relying on Mahalanobis distance... | 4.29 | 48% | See Reviews | View AI Dashboard |