SSL clustering is derived as KL-divergence optimization where a teacher-distribution constraint normalizes via inverse cluster priors and simplifies to batch centering by Jensen's inequality.
The international journal of robotics research , volume=
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
SplAttN uses Gaussian soft splatting and attention to avoid sparse projection collapse in point cloud completion, achieving SOTA results and demonstrating genuine visual cue reliance on KITTI.
citing papers explorer
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Information theoretic underpinning of self-supervised learning by clustering
SSL clustering is derived as KL-divergence optimization where a teacher-distribution constraint normalizes via inverse cluster priors and simplifies to batch centering by Jensen's inequality.
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SplAttN: Bridging 2D and 3D with Gaussian Soft Splatting and Attention for Point Cloud Completion
SplAttN uses Gaussian soft splatting and attention to avoid sparse projection collapse in point cloud completion, achieving SOTA results and demonstrating genuine visual cue reliance on KITTI.