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FuseMoE: Mixture-of-Experts Transformers for Fleximodal Fusion

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arxiv 2402.03226 v4 pith:LYKYQ6DI submitted 2024-02-05 cs.LG

Xing Han , Huy Nguyen , Carl Harris , Nhat Ho , Suchi Saria This is my paper
classification cs.LG
keywords fusemoedatamodalitiesdiversefunctiongatingmissingmixture-of-experts
verification ladder T0 review T1 audit T2 compute T3 formal
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As machine learning models in critical fields increasingly grapple with multimodal data, they face the dual challenges of handling a wide array of modalities, often incomplete due to missing elements, and the temporal irregularity and sparsity of collected samples. Successfully leveraging this complex data, while overcoming the scarcity of high-quality training samples, is key to improving these models' predictive performance. We introduce ``FuseMoE'', a mixture-of-experts framework incorporated with an innovative gating function. Designed to integrate a diverse number of modalities, FuseMoE is effective in managing scenarios with missing modalities and irregularly sampled data trajectories. Theoretically, our unique gating function contributes to enhanced convergence rates, leading to better performance in multiple downstream tasks. The practical utility of FuseMoE in the real world is validated by a diverse set of challenging prediction tasks.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. MuteBench: Modality Unavailability Tolerance Evaluation for Incomplete Multimodal Fusion

    cs.LG 2026-05 unverdicted novelty 7.0 of 10

    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.

  2. Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate

    cs.LG 2025-05 unverdicted novelty 7.0 of 10

    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.

  3. Kepler-Encoder-v0.1: Towards a Multimodal Embedding Model for Robots

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    A self-supervised multimodal encoder trained with vision, proprioception, and force yields a vision-only latent that recovers end-effector state and force above vision baselines on RH20T, with modest absolute force accuracy.

  4. Retrieval-Augmented Personalization with Foundation Models for Wearable Stress Detection

    cs.LG 2026-06 unverdicted novelty 6.0 of 10

    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.

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