BrainFIBRE pretrains a five-expert Mixture-of-Experts model on NODDI-derived microstructural maps and outperforms prior deep models on age, sex, cerebrovascular, neurodegenerative, and cognitive prediction.
arXiv preprint arXiv:2505.19190 (2025)
7 Pith papers cite this work. Polarity classification is still indexing.
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PromptDx adds a differentiable adapter to align multimodal data with a pre-trained TabPFN-style ICL engine, achieving strong Alzheimer's diagnosis performance with only 1% context samples.
R²ScP recovers missing audio-visual data in question answering by retrieving semantically consistent examples and purifying noise, outperforming generative imputation in incomplete scenarios.
SynIB is an information-theoretic objective that adds a penalty for unimodal confidence to standard task loss, improving accuracy on synergy-dependent examples by up to 7.8% across synthetic XOR tasks and five real-world multimodal benchmarks.
A literature survey that categorizes how Mixture-of-Experts architectures address multimodal learning challenges and identifies open research gaps.
A framework unifies multimodal intent interpretation, interaction-centric explainability, and agency-preserving controls as interdependent requirements for trustworthy Human-AI collaboration.
CoGR-MoE improves VQA by using concept-guided expert routing with option feature reweighting and contrastive learning to achieve consistent yet flexible reasoning across answer options.
citing papers explorer
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BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure
BrainFIBRE pretrains a five-expert Mixture-of-Experts model on NODDI-derived microstructural maps and outperforms prior deep models on age, sex, cerebrovascular, neurodegenerative, and cognitive prediction.
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PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis
PromptDx adds a differentiable adapter to align multimodal data with a pre-trained TabPFN-style ICL engine, achieving strong Alzheimer's diagnosis performance with only 1% context samples.
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Retrieving to Recover: Towards Incomplete Audio-Visual Question Answering via Semantic-consistent Purification
R²ScP recovers missing audio-visual data in question answering by retrieving semantically consistent examples and purifying noise, outperforming generative imputation in incomplete scenarios.
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SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning
SynIB is an information-theoretic objective that adds a penalty for unimodal confidence to standard task loss, improving accuracy on synergy-dependent examples by up to 7.8% across synthetic XOR tasks and five real-world multimodal benchmarks.
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Tackling Multimodal Learning Challenges with Mixture-of-Expert: A Survey
A literature survey that categorizes how Mixture-of-Experts architectures address multimodal learning challenges and identifies open research gaps.
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Toward a Unified Framework for Collaborative Design of Human-AI Interaction
A framework unifies multimodal intent interpretation, interaction-centric explainability, and agency-preserving controls as interdependent requirements for trustworthy Human-AI collaboration.
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CoGR-MoE: Concept-Guided Expert Routing with Consistent Selection and Flexible Reasoning for Visual Question Answering
CoGR-MoE improves VQA by using concept-guided expert routing with option feature reweighting and contrastive learning to achieve consistent yet flexible reasoning across answer options.