Summary of A Complexity Map Of Probabilistic Reasoning For Neurosymbolic Classification Techniques, by Arthur Ledaguenel et al.
A Complexity Map of Probabilistic Reasoning for Neurosymbolic Classification Techniques
by Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Complexity (cs.CC); Machine Learning (cs.LG); Symbolic Computation (cs.SC)
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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 The paper presents a unified formalism for four probabilistic reasoning problems in informed multi-label classification, a sub-field of neurosymbolic artificial intelligence. The authors aim to develop scalable techniques by understanding the asymptotic complexity of probabilistic reasoning, which is crucial as the number of classes increases. They compile known and new tractability results into a single complexity map, enabling practitioners to navigate the scalability landscape of probabilistic neurosymbolic techniques. Specifically, they focus on combining neural network learning capabilities with symbolic systems’ reasoning abilities, leveraging prior knowledge to improve neural classification systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using artificial intelligence (AI) to help computers make better decisions by considering what we already know. It’s like having a super-smart assistant that can learn from experience and use common sense. The researchers want to figure out how to scale this up, so they can apply it to more complex problems. They’re doing this by creating a map that shows which techniques work well for certain types of problems and which ones get stuck. This will help people working on AI projects know what to expect and make better decisions. |
Keywords
* Artificial intelligence * Classification * Neural network
