PyTorch, Explain! A Python library for Logic Explained Networks
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Abstract (arXiv)
"PyTorch, Explain!" is a Python module integrating a variety of state-of-the-art approaches to provide logic explanations from neural networks. This package focuses on bringing these methods to non-specialists. It has minimal dependencies and it is distributed under the Apache 2.0 licence allowing both academic and commercial use. Source code and documentation can be downloaded from the github repository: https://github.com/pietrobarbiero/pytorch_explain.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 5 of its 22 references that are in the map. First posted on arXiv 25 May 2021.
Builds on
5 of 22 references.
References · 5
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2020Concept Bottleneck Modelscited by 1,022
- 2020Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Surveycited by 252
- 2020Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claimscited by 163
- 2020Now You See Me (CME): Concept-based Model Extractioncited by 51
Built on by
Citing papers per year: 2021: 3; 2022: 1; 2023: 0; 2024: 0; 2025: 1.
5 papers.
- 2021Entropy-based Logic Explanations of Neural Networkscited by 51
- 2021Logic Explained Networkscited by 45
- 2021Algorithmic Concept-based Explainable Reasoningcited by 12
- 2022Learning Interpretable, High-Performing Policies for Autonomous Drivingcited by 11
- 2025Logic Explanation of AI Classifiers by Categorical Explaining Functorscited by 1
- 2025Logic Explanation of AI Classifiers by Categorical Explaining Functors20 Mar 2025
- 2022Learning Interpretable, High-Performing Policies for Autonomous Driving4 Feb 2022
- 2021Logic Explained Networks11 Aug 2021
- 2021Algorithmic Concept-based Explainable Reasoning15 Jul 2021
- 2021Entropy-based Logic Explanations of Neural Networks12 Jun 2021