Summary of Improving Neural-based Classification with Logical Background Knowledge, by Arthur Ledaguenel et al.
Improving Neural-based Classification with Logical Background Knowledge
by Arthur Ledaguenel, Céline Hudelot, Mostepha Khouadjia
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Symbolic Computation (cs.SC)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 new formalism for supervised multi-label classification with propositional background knowledge, blending the strengths of neurosymbolic AI. The authors introduce “semantic conditioning at inference,” which constrains the system during inference without affecting training. This technique outperforms two other popular approaches: semantic conditioning and semantic regularization. To evaluate its benefits, the paper develops a multi-scale methodology for assessing the impact of neurosymbolic techniques on model performance across various scales. Experimental results demonstrate that “semantic conditioning at inference” can create more accurate neural-based systems with fewer resources while ensuring output consistency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is all about combining two powerful tools in artificial intelligence: neural networks and symbolic reasoning. Imagine you’re trying to classify images as dogs or cats, but you also know some basic rules about what makes an animal a dog or cat. The authors propose a new way to do this called “semantic conditioning at inference.” It’s like adding a filter to your neural network that only kicks in during the final decision-making process. They compare this approach with two others and find that it produces more accurate results while using fewer resources. This is important because it could help create better AI systems that can understand and reason about the world around us. |
Keywords
* Artificial intelligence * Classification * Inference * Neural network * Regularization * Supervised
