deepluq

Library for Deep Learning Uncertainty Quantification

https://github.com/chengjie-lu/deepluq

Science Score: 54.0%

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CITATION.cff file
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Links to: arxiv.org
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Scientific vocabulary similarity
Low similarity (5.2%) to scientific vocabulary

Last synced: 7 months ago · JSON representation ·

Repository

Library for Deep Learning Uncertainty Quantification

Basic Info

Host: GitHub
Owner: chengjie-lu
License: other
Language: Python
Default Branch: main
Homepage: https://test.pypi.org/project/deepluq/0.1/
Size: 136 KB

Statistics

Stars: 0
Watchers: 2
Forks: 0
Open Issues: 0
Releases: 1

Created about 1 year ago · Last pushed 7 months ago

Metadata Files

Readme License Citation

DeepLUQ: Python Library for Deep Learning Uncertainty Quantification

This library provides methods and metrics for capturing and quantifying uncertainty in deep learning models. It currently includes Monte Carlo Dropout (MC-Dropout) as the UQ method for capturing uncertainties in model predictions. The library integrates various UQ metrics to quantify uncertainties of two common deep-learning tasks, i.e., regression and classification.

Case Study Applications

Assessing the Uncertainty and Robustness of the Laptop Refurbishing Software

Github repository: https://github.com/chengjie-lu/sticker-detection-uncertainty-quantification

Paper: Chengjie Lu, Jiahui Wu, Shaukat Ali, and Mikkel Labori Olsen. "Assessing the Uncertainty and Robustness of the Laptop Refurbishing Software". In 18th IEEE International Conference on Software Testing, Verification and Validation (ICST) 2025. Preprint

Evaluating Uncertainty and Quality of Visual Language Action-enabled Robots

Github repository: https://github.com/pablovalle/VLA_UQ

Paper: Valle, Pablo, Chengjie Lu, Shaukat Ali, and Aitor Arrieta. "Evaluating Uncertainty and Quality of Visual Language Action-enabled Robots." arXiv preprint arXiv:2507.17049 (2025). Preprint

References

Gal, Yarin, and Zoubin Ghahramani. "Dropout as a bayesian approximation: Representing model uncertainty in deep learning." international conference on machine learning. PMLR, 2016.

Gal, Yarin. "Uncertainty in deep learning." (2016).

Catak, Ferhat Ozgur, Tao Yue, and Shaukat Ali. "Prediction surface uncertainty quantification in object detection models for autonomous driving." 2021 IEEE International Conference on Artificial Intelligence Testing (AITest). IEEE, 2021.

Owner

Login: chengjie-lu
Kind: user

Repositories: 1
Profile: https://github.com/chengjie-lu

Citation (CITATION.cff)

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Lu"
  given-names: "Chengjie"
  orcid: "https://orcid.org/0000-0002-5818-7547"
- family-names: "Roehr"
  given-names: "Thomas"
  orcid: "https://orcid.org/0000-0002-7715-705"
title: "DeepLUQ: Python Library for Deep Learning Uncertainty Quantification"
version: 0.1
date-released: 2025-01-06
license: INTO-CPS
url: "https://github.com/chengjie-lu/DeepLUQ"

GitHub Events

Total

Release event: 1
Push event: 12
Pull request review event: 1
Pull request event: 3
Create event: 6

Last Year

Release event: 1
Push event: 12
Pull request review event: 1
Pull request event: 3
Create event: 6

Issues and Pull Requests

Last synced: 7 months ago

All Time

Total issues: 0
Total pull requests: 3
Average time to close issues: N/A
Average time to close pull requests: about 4 hours
Total issue authors: 0
Total pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

Past Year

Issues: 0
Pull requests: 3
Average time to close issues: N/A
Average time to close pull requests: about 4 hours
Issue authors: 0
Pull request authors: 1
Average comments per issue: 0
Average comments per pull request: 0.0
Merged pull requests: 1
Bot issues: 0
Bot pull requests: 0

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