Summary of Semantic Objective Functions: a Distribution-aware Method For Adding Logical Constraints in Deep Learning, by Miguel Angel Mendez-lucero et al.
Semantic Objective Functions: A distribution-aware method for adding logical constraints in deep learning
by Miguel Angel Mendez-Lucero, Enrique Bojorquez Gallardo, Vaishak Belle
First submitted to arxiv on: 3 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG); Logic in Computer Science (cs.LO)
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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 proposes a novel framework for constrained learning, which addresses the growing concerns about safety, explainability, and efficiency in machine learning systems. Building upon Symbolic Constrained Learning and Knowledge Distillation techniques, the authors provide a theoretical construction that generalizes many existing approaches. The proposed loss-based method embeds knowledge and logical constraints into a neural network model that outputs probability distributions. This is achieved by constructing a distribution from external knowledge or logic formulas and defining a loss function as a combination of the original loss function with the Fisher-Rao distance or Kullback-Leibler divergence to the constraint distribution. The authors demonstrate their method on various learning tasks, including classification with logical constraints, transferring knowledge from logic formulas, and knowledge distillation from general distributions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make machine learning systems more responsible and understandable. It combines two existing techniques called Symbolic Constrained Learning and Knowledge Distillation. The authors want to make sure that the decisions made by these systems are correct and easy to understand. They propose a new way of building these systems that includes rules and logical constraints. This approach can be used in different areas, such as classification, transferring knowledge, and distilling information from general distributions. |
Keywords
» Artificial intelligence » Classification » Knowledge distillation » Loss function » Machine learning » Neural network » Probability
