Time series forecasting with PyTorch

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

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.

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

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

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

(ICCV 2019) Uncertainty-aware Face Representation and Recognition

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

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

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

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.

Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning, ICLR 2020

The DESPOT online POMDP solver

"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning" by Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah (ICLR 2021)