Summary of Ltntorch: Pytorch Implementation Of Logic Tensor Networks, by Tommaso Carraro et al.
LTNtorch: PyTorch Implementation of Logic Tensor Networks
by Tommaso Carraro, Luciano Serafini, Fabio Aiolli
First submitted to arxiv on: 24 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Logic Tensor Networks (LTN) is a Neuro-Symbolic framework that combines deep learning and logical reasoning to enable learning by logical reasoning. The framework defines a logical knowledge base as the objective of a neural model, allowing for optimization via gradient-descent optimization. Fuzzy logic enables continuous truth values in the interval [0,1], making learning possible. LTNtorch is the PyTorch implementation of Logic Tensor Networks. This paper formalizes LTN and provides a basic binary classification example. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Logic Tensor Networks (LTN) helps computers learn by thinking logically. It’s like combining human reasoning with machine learning. This framework defines what it knows about a task, then uses that to train a model. The training process involves three steps: grounding the formulas in data, evaluating the formulas, and adjusting the model to match what it knows. |
Keywords
» Artificial intelligence » Classification » Deep learning » Gradient descent » Grounding » Knowledge base » Machine learning » Optimization
