Time series forecasting with PyTorch

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

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

A Library for Uncertainty Quantification.

Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.

Visualizations of distributions and uncertainty

Lightweight, useful implementation of conformal prediction on real data.

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

Learn fast, scalable, and calibrated measures of uncertainty using neural networks!

A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.

A 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.

A state-of-the-art distributed system using Reactive DDD as uncertainty modeling, Event Storming as subdomain decomposition, Event Sourcing as an eventual persistence mechanism, CQRS, Async Projections, Microservices for individual deployable units, Event-driven Architecture for efficient integration, and Clean Architecture as domain-centric design

Asynchronous Multiple LiDAR-Inertial Odometry using Point-wise Inter-LiDAR Uncertainty Propagation

A curated list of trustworthy deep learning papers. Daily updating...

(ICCV 2019) Uncertainty-aware Face Representation and Recognition

Open-source framework for uncertainty and deep learning models in PyTorch 🌱

Self-Supervised Learning for OOD Detection (NeurIPS 2019)

Uncertainty Quantification 360 (UQ360) is an extensible open-source toolkit that can help you estimate, communicate and use uncertainty in machine learning model predictions.

The DESPOT online POMDP solver

Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).