# References

## Key Papers on Uncertainty Quantification

### Foundational Work

1. **Gal, Y., & Ghahramani, Z.** (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. *International Conference on Machine Learning* (ICML).

2. **Lakshminarayanan, B., Pritzel, A., & Blundell, C.** (2017). Simple and scalable predictive uncertainty estimation using deep ensembles. *Advances in Neural Information Processing Systems* (NeurIPS).

3. **Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q.** (2017). On calibration of modern neural networks. *International Conference on Machine Learning* (ICML).

### Bayesian Deep Learning

4. **Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D.** (2015). Weight uncertainty in neural networks. *International Conference on Machine Learning* (ICML).

5. **Graves, A.** (2011). Practical variational inference for neural networks. *Advances in Neural Information Processing Systems* (NeurIPS).

6. **Hernández-Lobato, J. M., & Adams, R.** (2015). Probabilistic backpropagation for scalable learning of Bayesian neural networks. *International Conference on Machine Learning* (ICML).

### Ensemble Methods

7. **Dietterich, T. G.** (2000). Ensemble methods in machine learning. *International Workshop on Multiple Classifier Systems*.

8. **Fort, S., Hu, H., & Lakshminarayanan, B.** (2019). Deep ensembles: A loss landscape perspective. *arXiv preprint arXiv:1912.02757*.

### Calibration Methods

9. **Platt, J.** (1999). Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. *Advances in Large Margin Classifiers*.

10. **Zadrozny, B., & Elkan, C.** (2002). Transforming classifier scores into accurate multiclass probability estimates. *ACM SIGKDD International Conference on Knowledge Discovery and Data Mining*.

11. **Niculescu-Mizil, A., & Caruana, R.** (2005). Predicting good probabilities with supervised learning. *International Conference on Machine Learning* (ICML).

### Aleatoric and Epistemic Uncertainty

12. **Kendall, A., & Gal, Y.** (2017). What uncertainties do we need in Bayesian deep learning for computer vision? *Advances in Neural Information Processing Systems* (NeurIPS).

13. **Malinin, A., & Gales, M.** (2018). Predictive uncertainty estimation via prior networks. *Advances in Neural Information Processing Systems* (NeurIPS).

### Applications and Case Studies

14. **Leibig, C., Allken, V., Ayhan, M. S., Berens, P., & Wahl, S.** (2017). Leveraging uncertainty information from deep neural networks for disease detection. *Scientific Reports*.

15. **Michelmore, R., Kwiatkowska, M., & Gal, Y.** (2018). Evaluating uncertainty quantification in end-to-end autonomous driving control. *arXiv preprint arXiv:1811.06817*.

### Out-of-Distribution Detection

16. **Hendrycks, D., & Gimpel, K.** (2017). A baseline for detecting misclassified and out-of-distribution examples in neural networks. *International Conference on Learning Representations* (ICLR).

17. **Liang, S., Li, Y., & Srikant, R.** (2018). Enhancing the reliability of out-of-distribution image detection in neural networks. *International Conference on Learning Representations* (ICLR).

### Surveys and Reviews

18. **Abdar, M., et al.** (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. *Information Fusion*.

19. **Gawlikowski, J., et al.** (2021). A survey of uncertainty in deep neural networks. *arXiv preprint arXiv:2107.03342*.

20. **Hüllermeier, E., & Waegeman, W.** (2021). Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods. *Machine Learning*.

## Software and Tools

- **TensorFlow Probability**: <https://www.tensorflow.org/probability>
- **PyTorch Uncertainty**: <https://github.com/pytorch/uncertainty>
- **Uncertainty Toolbox**: <https://github.com/uncertainty-toolbox/uncertainty-toolbox>
- **NetCal**: Calibration library: <https://github.com/fabiankueppers/calibration-framework>

## Online Resources

- **Yarin Gal's Blog**: <http://www.cs.ox.ac.uk/people/yarin.gal/website/blog.html>
- **Uncertainty in Deep Learning**: PhD Thesis by Yarin Gal
- **Stanford CS236 Deep Generative Models**: Course materials on uncertainty

## Datasets and Benchmarks

- **UCI Machine Learning Repository**: Classification and regression datasets
- **Kaggle Uncertainty Quantification Challenges**: Various competitions
- **Medical Image Segmentation Datasets**: With uncertainty annotations
- **Autonomous Driving Datasets**: With rare event annotations