mlsanityData to 7 Oct 2026

Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits

arXiv 2403.03281 ↗First posted 5 Mar 2024cs.LG cs.AIcited by 5

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Abstract (arXiv)

We consider the problem of late multi-modal fusion for discriminative learning. Motivated by noisy, multi-source domains that require understanding the reliability of each data source, we explore the notion of credibility in the context of multi-modal fusion. We propose a combination function that uses probabilistic circuits (PCs) to combine predictive distributions over individual modalities. We also define a probabilistic measure to evaluate the credibility of each modality via inference queries over the PC. Our experimental evaluation demonstrates that our fusion method can reliably infer credibility while maintaining competitive performance with the state-of-the-art.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 59 references that are in the map. First posted on arXiv 5 Mar 2024.

Builds on

16 of 59 references.

References · 16

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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  3. 2017Multimodal Machine Learning: A Survey and Taxonomycited by 1,004
  4. 2018Evidential Deep Learning to Quantify Classification Uncertaintycited by 728
  5. 2019Neural Probabilistic Logic Programming in DeepProbLogcited by 389
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  7. 2020Einsum Networks: Fast and Scalable Learning of Tractable Probabilistic Circuitscited by 102
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  9. 2018CentralNet: a Multilayer Approach for Multimodal Fusioncited by 86
  10. 2019MFAS: Multimodal Fusion Architecture Searchcited by 84
  11. 2023Provable Dynamic Fusion for Low-Quality Multimodal Datacited by 81
  12. 2024Reliable Conflictive Multi-View Learningcited by 45
4 more
  1. 2020Strudel: Learning Structured-Decomposable Probabilistic Circuitscited by 24
  2. 2019UNO: Uncertainty-aware Noisy-Or Multimodal Fusion for Unanticipated Input Degradationcited by 17
  3. 2024Building Expressive and Tractable Probabilistic Generative Models: A Reviewcited by 12
  4. 2013A New Look at Causal Independencecited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 2; 2026: 3.

5 papers.

  1. 2025Tractable Representation Learning with Probabilistic Circuitscited by 4
  2. 2026Context-specific Credibility-aware Multimodal Fusion with Conditional Probabilistic Circuitscited by 2
  3. 2025Late Fusion Multi-task Learning for Semiparametric Inference with Nuisance Parameterscited by 1
  4. 2026Geometry-Aware Probabilistic Circuits via Voronoi Tessellationscited by 1
  5. 2026Reliable Fusion of Conflicting Expertscited by 0