Metadata-Version: 2.1
Name: deepluq
Version: 0.1.4
Summary: Package to quantify uncertainty in deep learning models
Author-email: Chengjie Lu <chengjielu@simula.no>
Maintainer-email: Chengjie Lu <chengjielu@simula.no>
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Project-URL: homepage, https://github.com/chengjie-lu/deepluq
Project-URL: documentation, https://github.com/chengjie-lu/deepluq
Project-URL: repository, https://github.com/chengjie-lu/deepluq
Keywords: deep learning,uncertainty quantification,machine learning
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Build Tools
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.12
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# 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](https://arxiv.org/pdf/2409.03782)

### 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](https://arxiv.org/pdf/2507.17049)



## 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.
