Summary of Nesya: Neurosymbolic Automata, by Nikolaos Manginas et al.
NeSyA: Neurosymbolic Automata
by Nikolaos Manginas, George Paliouras, Luc De Raedt
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper proposes Neuro Symbolic Automata (NeSyA) as a novel approach for integrating low-level perception with high-level reasoning in Neurosymbolic Artificial Intelligence (NeSy). The authors address the lack of attention towards developing NeSy systems tailored to temporal/sequential problems, which involve reasoning symbolically over sequences of subsymbolic observations. They demonstrate that probabilistic semantics symbolic automata can efficiently and differentially reason over subsymbolic sequences, outperforming existing NeSy approaches on problems with a temporal component. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using artificial intelligence to understand patterns in things like videos or audio recordings. Right now, these systems are not very good at handling time-related information, but the researchers propose a new way of doing things that could make them better. They call this approach Neuro Symbolic Automata (NeSyA). It’s like a special kind of computer program that can look at small pieces of information and use logic to understand what’s going on over time. |
Keywords
» Artificial intelligence » Attention » Semantics
