Summary of A Mathematical Framework, a Taxonomy Of Modeling Paradigms, and a Suite Of Learning Techniques For Neural-symbolic Systems, by Charles Dickens et al.
A Mathematical Framework, a Taxonomy of Modeling Paradigms, and a Suite of Learning Techniques for Neural-Symbolic Systems
by Charles Dickens, Connor Pryor, Changyu Gao, Alon Albalak, Eriq Augustine, William Wang, Stephen Wright, Lise Getoor
First submitted to arxiv on: 12 Jul 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 The paper introduces Neural-Symbolic Energy-Based Models (NeSy-EBMs), a unified mathematical framework for discriminative and generative modeling that encompasses both probabilistic and non-probabilistic approaches. This framework enables the development of a taxonomy of modeling paradigms, focusing on neural-symbolic interfaces and reasoning capabilities. The authors also introduce a suite of learning techniques for NeSy-EBMs, including bilevel and stochastic policy optimization. Furthermore, they provide general expressions for gradients of prominent learning losses, leveraging methods from multiple domains. The paper concludes with the introduction of Neural Probabilistic Soft Logic (NeuPSL), an open-source library designed for scalability and expressivity, allowing for real-world applications of NeSy systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about a new way to do artificial intelligence called Neural-Symbolic Energy-Based Models. This method helps us understand how different AI approaches work together and how they can learn from each other. The authors also share some new techniques that make it easier to train these models, which is important for making AI more useful in real-life applications like recognizing images or understanding language. |
Keywords
* Artificial intelligence * Optimization
