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.
arXiv preprint arXiv:2209.03430 (2022)
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Cross-modal alignment on a fixed graph splits into global hardness and sheaf-Laplacian obstruction, with an explicit ReLU construction showing staged alignment can need quadratically less width than direct alignment.
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+ extends a prior game recommender with signed edge reweighting and LLM-generated player/game descriptions, reporting slight accuracy and diversity gains on two Steam datasets.
citing papers explorer
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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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Sheaf-Laplacian Obstruction and Projection Hardness for Cross-Modal Compatibility on a Modality-Independent Site
Cross-modal alignment on a fixed graph splits into global hardness and sheaf-Laplacian obstruction, with an explicit ReLU construction showing staged alignment can need quadratically less width than direct alignment.
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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+ extends a prior game recommender with signed edge reweighting and LLM-generated player/game descriptions, reporting slight accuracy and diversity gains on two Steam datasets.