Summary of Credibility-aware Multi-modal Fusion Using Probabilistic Circuits, by Sahil Sidheekh et al.
Credibility-Aware Multi-Modal Fusion Using Probabilistic Circuits
by Sahil Sidheekh, Pranuthi Tenali, Saurabh Mathur, Erik Blasch, Kristian Kersting, Sriraam Natarajan
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of combining information from multiple sources (multi-modal fusion) to improve machine learning models. They focus on situations where data is noisy and comes from different places, requiring them to understand which source is most reliable. To address this, they propose a method that uses probabilistic circuits to combine predictions from individual sources. This approach also allows them to evaluate the credibility of each source by asking questions about its reliability. The results show that their method can accurately assess credibility while still performing well compared to existing state-of-the-art techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about combining different types of data (like images, audio, and text) to make better predictions. Imagine you’re trying to figure out what someone is saying based on a video of them speaking, an image of their face, and some written notes. This paper shows how to combine these different types of information in a way that makes sense, even when the data is noisy or unreliable. |
Keywords
* Artificial intelligence * Machine learning * Multi modal
