ICLR 2026 - Submissions
Submissions
Summary Statistics
| Quantity AI Content | Count | Avg Rating |
|---|---|---|
| 0-10% | 1 (100%) | 2.67 |
| 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%) | 2.67 |
| Title | Abstract | Avg Rating | Quantity AI Content | Reviews | Pangram Dashboard |
|---|---|---|---|---|---|
| Heteroscedastic Variational Bayesian Last Layers: Modeling Input-Dependent Noise in Sparse-Data Regression | Bayesian Neural Networks (BNNs) have been extensively studied for uncertainty quantification. To train BNNs efficiently, Variational Bayesian Last Layer (VBLL) provides a sampling-free, deterministic ... | 2.67 | 6% | See Reviews | View AI Dashboard |