Summary of Explain, Agree, Learn: Scaling Learning For Neural Probabilistic Logic, by Victor Verreet et al.
EXPLAIN, AGREE, LEARN: Scaling Learning for Neural Probabilistic Logic
by Victor Verreet, Lennert De Smet, Luc De Raedt, Emanuele Sansone
First submitted to arxiv on: 15 Aug 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 A neural probabilistic logic system combines neural networks’ learning capabilities with probabilistic logic’s robustness, following the neuro-symbolic paradigm. To optimize likelihood exactly, expensive probabilistic logic inference is required, which limits scalability. This paper proposes optimizing a sampling-based objective instead, proving that it has bounded error relative to the likelihood, which vanishes as sample count increases. The new concept of sample diversity accelerates this convergence. The EXPLAIN, AGREE, LEARN (EXAL) method uses this objective, explaining data, reweighting explanations in concordance with neural components, and learning from reweighed signals. Unlike previous neuro-symbolic methods, EXAL can scale to larger problem sizes while maintaining theoretical error guarantees. Experimental results verify these claims and show EXAL outperforming recent neuro-symbolic methods on MNIST addition and Warcraft pathfinding problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of combining artificial intelligence and logical reasoning is being developed. This method, called neural probabilistic logic, helps computers learn from data while also understanding the reasons behind their decisions. Right now, this method can be slow and inefficient for large problems. The researchers in this paper have found a way to make it faster and more efficient by using a new approach that involves sampling and reweighting explanations. This new approach is called EXPLAIN, AGREE, LEARN (EXAL), and it’s able to handle larger problem sizes while still providing accurate results. This method has the potential to be very useful in fields such as image recognition, decision-making, and game playing. |
Keywords
» Artificial intelligence » Inference » Likelihood
