ICLR 2026 - Submissions

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Submissions

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Avg Rating: 0-11-22-33-44-55-66-77-88-99-10All

Summary Statistics

Quantity AI Content Count Avg Rating
0-10% 1 (100%) 5.50
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%) 5.50
Title Abstract Avg Rating Quantity AI Content Reviews Pangram Dashboard
SeMa3D: Lifting Vision-Language Models for Unsupervised 3D Semantic Correspondence We tackle unsupervised dense semantic correspondence for 3D shapes, focusing on severe \textbf{non-isometric} deformations and \textbf{inter-class} matching--a regime where conventional functional map... 5.50 0% See Reviews View AI Dashboard
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