Summary of Convex and Bilevel Optimization For Neuro-symbolic Inference and Learning, by Charles Dickens et al.
Convex and Bilevel Optimization for Neuro-Symbolic Inference and Learning
by Charles Dickens, Changyu Gao, Connor Pryor, Stephen Wright, Lise Getoor
First submitted to arxiv on: 17 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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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 presents a novel gradient-based parameter learning framework for neural-symbolic (NeSy) systems, specifically designed for state-of-the-art NeSy architecture NeuPSL. The authors leverage convex and bilevel optimization techniques to develop a smooth primal and dual formulation of NeuPSL inference, allowing for efficient computation of learning gradients as functions of optimal dual variables. A dual block coordinate descent algorithm is also proposed, which naturally exploits warm-starts, resulting in over 100x learning runtime improvements compared to the current best NeuPSL inference method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way for computers to learn from neural-symbolic systems. Neural-symbolic systems combine the strengths of artificial intelligence and logical reasoning. The authors developed a special algorithm that makes it faster and more efficient to train these systems. They tested their approach on eight different datasets, achieving up to 16% better results than other methods. |
Keywords
* Artificial intelligence * Inference * Optimization
