Summary of Deep Neural Network For Constraint Acquisition Through Tailored Loss Function, by Eduardo Vyhmeister et al.
Deep Neural Network for Constraint Acquisition through Tailored Loss Function
by Eduardo Vyhmeister, Rocio Paez, Gabriel Gonzalez
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Symbolic Computation (cs.SC)
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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 addresses a significant gap in learning constraints from data, which has far-reaching implications for real-world problem-solving. While modeling and solving under constraints are well-studied areas, the methods for acquiring these constraints from data remain limited. The authors propose a novel approach using Deep Neural Networks (DNNs) to extract constraints directly from datasets, leveraging Symbolic Regression techniques with tailored loss functions. By achieving direct formulation of constraints, this work opens up new possibilities for automating constraint acquisition, which could be applied to various domains and tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about learning rules or “rules” from data that can help us solve problems better. Right now, we don’t have good ways to do this, so the authors came up with a new approach using special kinds of computer programs called Deep Neural Networks (DNNs). They used these DNNs to figure out the rules directly from data, which could make it easier for computers and people to solve problems. This might be useful in many areas like science, engineering, or business. |
Keywords
* Artificial intelligence * Regression
