# Uncertainty-Wizard: Fast and User-Friendly Neural Network Uncertainty Quantification

arXiv: https://arxiv.org/abs/2101.00982 · HTML: https://mlsanity.com/paper/2101.00982/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Dec 2020.

## Built on by (most cited first, 9 of 9)

- [Simple Techniques Work Surprisingly Well for Neural Network Test Prioritization and Active Learning (Replicability Study)](https://mlsanity.com/paper/2205.00664/) (2022)
- [Fail-Safe Execution of Deep Learning based Systems through Uncertainty Monitoring](https://mlsanity.com/paper/2102.00902/) (2021)
- [Predicting Safety Misbehaviours in Autonomous Driving Systems using Uncertainty Quantification](https://mlsanity.com/paper/2404.18573/) (2024)
- [Prediction Surface Uncertainty Quantification in Object Detection Models for Autonomous Driving](https://mlsanity.com/paper/2107.04991/) (2021)
- [Generating and Detecting True Ambiguity: A Forgotten Danger in DNN Supervision Testing](https://mlsanity.com/paper/2207.10495/) (2022)
- [A Survey on Uncertainty Toolkits for Deep Learning](https://mlsanity.com/paper/2205.01040/) (2022)
- [Uncertainty Quantification for Deep Neural Networks: An Empirical Comparison and Usage Guidelines](https://mlsanity.com/paper/2212.07118/) (2022)
- [CheapET-3: Cost-Efficient Use of Remote DNN Models](https://mlsanity.com/paper/2208.11552/) (2022)
- [Adopting Two Supervisors for Efficient Use of Large-Scale Remote Deep Neural Networks](https://mlsanity.com/paper/2304.02654/) (2023)

## Abstract (arXiv)

Uncertainty and confidence have been shown to be useful metrics in a wide variety of techniques proposed for deep learning testing, including test data selection and system supervision.We present uncertainty-wizard, a tool that allows to quantify such uncertainty and confidence in artificial neural networks. It is built on top of the industry-leading tf.keras deep learning API and it provides a near-transparent and easy to understand interface. At the same time, it includes major performance optimizations that we benchmarked on two different machines and different configurations.