feature_interpretability
Tools for neural network interpretability by examining internal model states (features) in both Python TensorFlow and PyTorch
Science Score: 39.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (7.6%) to scientific vocabulary
Repository
Tools for neural network interpretability by examining internal model states (features) in both Python TensorFlow and PyTorch
Basic Info
Statistics
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
[//]: <> (THIS IS A MARKDOWN FILE, VIEW IN A MARKDOWN VIEWER OR CONVERT)
Feature Interpretability
This folder of code contains tools for extracting, plotting, and computing with features extracted from nerual networks. While these tools are intended to be easily maluable to new networks and new datasets, they are not guarenteed to work outside of these settings.
These tools were developed by Skylar Callis. They developed this code while working as a post-bachelors student at Los Alamos National Laboratory (LANL) from 2022 - 2024. To see what they are up to these days, visit Skylar's Website .
The feature interpertability code has been approved by LANL for a BSD-3 open source license under O#4675.
The documentation for this code can be found locally and hosted on GitHub.
The GitHub page for this code can be found at here.
More details about the use of feature interpretability on the LANL coupon problem can be found in:
Hickmann, K, Callis, S, & Andrews, S. Training and Interpretability of Deep-Neural Methods for Damage Calibration in Copper. Proceedings of the ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium. ASME 2023 Verification, Validation, and Uncertainty Quantification Symposium. Baltimore, Maryland, USA. May 1719, 2023. V001T04A001. ASME. https://doi.org/10.1115/VVUQ2023-108759
Owner
- Name: Los Alamos National Laboratory
- Login: lanl
- Kind: organization
- Email: github-register@lanl.gov
- Location: Los Alamos, New Mexico, USA
- Website: https://www.lanl.gov/
- Repositories: 224
- Profile: https://github.com/lanl
GitHub Events
Total
Last Year
Issues and Pull Requests
Last synced: 12 months ago
All Time
- Total issues: 0
- Total pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Total issue authors: 0
- Total pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0