Summary of Differentiable Logic Programming For Distant Supervision, by Akihiro Takemura et al.
Differentiable Logic Programming for Distant Supervision
by Akihiro Takemura, Katsumi Inoue
First submitted to arxiv on: 22 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 introduces a novel method for integrating neural networks with logic programming in Neural-Symbolic AI (NeSy) to learn with distant supervision, where direct labels are unavailable. The approach embeds neural network outputs and logic programs into matrices, evaluating logical implications and constraints in a differentiable manner. This allows for more efficient learning under distant supervision, unlike prior methods that rely on symbolic solvers. The method is evaluated against existing approaches while maintaining a constant volume of training data, showing improved accuracy and speed in various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to learn from information where the correct answers aren’t directly labeled. They created a new way to combine neural networks (which are really good at recognizing patterns) with logic programming (which is great at understanding rules). Instead of using special programs to figure out what’s missing, they use a special matrix that can be changed easily. This helps computers learn faster and more accurately from incomplete information. |
Keywords
» Artificial intelligence » Neural network
