Summary of On the Independence Assumption in Neurosymbolic Learning, by Emile Van Krieken et al.
On the Independence Assumption in Neurosymbolic Learning
by Emile van Krieken, Pasquale Minervini, Edoardo M. Ponti, Antonio Vergari
First submitted to arxiv on: 12 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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Summary difficulty | Written by | Summary |
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High | Paper authors | High Difficulty Summary Read the original abstract here |
Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study critiques a common assumption in state-of-the-art neurosymbolic learning systems that simplify learning and reasoning by assuming conditionally independent symbols given input. The authors prove that this assumption leads to overconfident predictions, inability to represent uncertainty, and difficulty in optimizing loss functions. To address these limitations, the study provides theoretical foundations for designing more expressive neurosymbolic probabilistic models. |
Low | GrooveSquid.com (original content) | Low Difficulty Summary Neurosymbolic learning systems use logic to guide neural networks. A common shortcut is assuming symbols are independent given input. This makes it easy to learn and reason, but also causes problems. The paper shows that this assumption makes the system overconfident, unable to represent uncertainty, and hard to optimize. It gives ideas for creating more powerful models. |