ICLR 2026 - Submissions
Submissions
Summary Statistics
| Quantity AI Content | Count | Avg Rating |
|---|---|---|
| 0-10% | 0 (0%) | N/A |
| 10-30% | 1 (100%) | 5.00 |
| 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%) | 5.00 |
| Title | Abstract | Avg Rating | Quantity AI Content | Reviews | Pangram Dashboard |
|---|---|---|---|---|---|
| Accurate and Efficient Singular Value Decomposition For LLMs via Decay-aware Rank Allocation and Feature-Preserved Weight Update | Singular Value Decomposition (SVD) provides a hardware-agnostic and effective paradigm for compressing and accelerating Large Language Models (LLMs) by decomposing and truncating weight matrices, foll... | 5.00 | 12% | See Reviews | View AI Dashboard |