Uncertainty Toolbox: a Python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

A Library for Uncertainty Quantification.

A collection of research and application papers of (uncertainty) calibration techniques.

A toolkit for visualizations in materials informatics.

Calibration library and code for the paper: Verified Uncertainty Calibration. Ananya Kumar, Percy Liang, Tengyu Ma. NeurIPS 2019 (Spotlight).

Code to accompany the paper 'Improving model calibration with accuracy versus uncertainty optimization'.

(ECCV 2022) BayesCap: Bayesian Identity Cap for Calibrated Uncertainty in Frozen Neural Networks

Code for evaluating uncertainty estimation methods for Transformer-based architectures in natural language understanding tasks.

A project to train your model from scratch or fine-tune a pretrained model using the losses provided in this library to improve out-of-distribution detection and uncertainty estimation performances. Calibrate your model to produce enhanced uncertainty estimations. Detect out-of-distribution data using the defined score type and threshold.

Source code for our paper: "LoGU: Long-form Generation with Uncertainty Expressions".

Calibration of Few-Shot Classification Tasks: Mitigating Misconfidence from Distribution Mismatch, IEEE Access vol.10

Service to examine data processing pipelines (e.g., machine learning or deep learning pipelines) for uncertainty consistency (calibration), fairness, and other safety-relevant aspects.

Truth Discovery Promotes Uncertainty Calibration of DNNs (UAI 2021)