Summary of Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules, By Jonathan Feldstein et al.
Efficiently Learning Probabilistic Logical Models by Cheaply Ranking Mined Rules
by Jonathan Feldstein, Dominic Phillips, Efthymia Tsamoura
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
GrooveSquid.com Paper Summaries
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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 proposes a framework called SPECTRUM for learning logical theories from relational data. Unlike neural networks, logical theories are handcrafted using domain expertise, making their development costly and prone to errors. The authors introduce precision and recall for logical rules and define rule utility as a cost-effective measure of predictive power. They also develop a scalable algorithm that mines recurrent subgraphs in the data graph and efficiently ranks rules derived from these subgraphs. This framework achieves better performance than neural networks on CPUs, with a runtime under 1% of state-of-the-art neural network approaches on GPUs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding ways to learn logical theories from data without needing a lot of expertise or expensive computing power. The current way of doing this is slow and not very accurate. The authors came up with a new method called SPECTRUM that can learn these theories much faster and more accurately than before. This will help make it possible to use logical theories in more real-world applications. |
Keywords
» Artificial intelligence » Neural network » Precision » Recall
