MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.
Fusemoe: Mixture-of- experts transformers for fleximodal fusion
3 Pith papers cite this work, alongside 2 external citations. Polarity classification is still indexing.
3
Pith papers citing it
2
external citations · external index
fields
cs.LG 3verdicts
UNVERDICTED 3representative citing papers
ConfSMoE adds expert-opinion imputation and detaches softmax routing scores to ground-truth task confidence to relieve expert collapse in SMoE without extra load-balance losses, evaluated on four real-world datasets.
Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.
citing papers explorer
-
MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion
MuteBench evaluates multimodal fusion robustness to modality missing and within-modality missing on 125000 samples from 9 clinical datasets, finding architecture family predicts tolerance better than parameter count.
-
Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate
ConfSMoE adds expert-opinion imputation and detaches softmax routing scores to ground-truth task confidence to relieve expert collapse in SMoE without extra load-balance losses, evaluated on four real-world datasets.
-
Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection
Retrieval from out-of-domain foundation models enables personalization of a lightweight transformer for stress detection, yielding +3.92% accuracy and +4.76% F1 gains on WESAD without user labels.