A cellular sheaf framework on a modality-independent site defines projection hardness and sheaf-Laplacian obstruction as distinct measures of cross-modal incompatibility, separating global mapping complexity from local consistency failures.
arXiv preprint arXiv:2209.03430 (2022)
4 Pith papers cite this work. Polarity classification is still indexing.
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AsyMoE adds hyperbolic geometry for cross-modal hierarchies and evidence-priority experts to address vision-language asymmetry in LVLMs, reporting 1.5% average gains and 25.45% fewer active parameters.
PTA framework purifies noisy multimodal data via meta-learning and distills cross-modal knowledge through diffusion to create robust single-modality models under missing modalities.
CPGRec+ improves game recommendations on Steam data by reweighting player-game edges with signed preference strengths and using LLMs to generate preference-aware descriptions, yielding higher accuracy and diversity than prior models.
citing papers explorer
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Sheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Site
A cellular sheaf framework on a modality-independent site defines projection hardness and sheaf-Laplacian obstruction as distinct measures of cross-modal incompatibility, separating global mapping complexity from local consistency failures.
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Hyperbolic and Evidence-Prioritized Experts for Large Vision-Language Models
AsyMoE adds hyperbolic geometry for cross-modal hierarchies and evidence-priority experts to address vision-language asymmetry in LVLMs, reporting 1.5% average gains and 25.45% fewer active parameters.
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Purify-then-Align: Towards Robust Human Sensing under Modality Missing with Knowledge Distillation from Noisy Multimodal Teacher
PTA framework purifies noisy multimodal data via meta-learning and distills cross-modal knowledge through diffusion to create robust single-modality models under missing modalities.
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CPGRec+: A Balance-oriented Framework for Personalized Video Game Recommendations
CPGRec+ improves game recommendations on Steam data by reweighting player-game edges with signed preference strengths and using LLMs to generate preference-aware descriptions, yielding higher accuracy and diversity than prior models.