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
Sensitivity Analysis Library in Python. Contains Sobol, Morris, FAST, and other methods.
A Library for Uncertainty Quantification.
Lightweight, useful implementation of conformal prediction on real data.
Visualizations of distributions and uncertainty
A professionally curated list of awesome Conformal Prediction videos, tutorials, books, papers, PhD and MSc theses, articles and open-source libraries.
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 3D vision library from 2D keypoints: monocular and stereo 3D detection for humans, social distancing, and body orientation.
Open-source framework for uncertainty and deep learning models in PyTorch 🌱
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
(ICCV 2019) Uncertainty-aware Face Representation and Recognition
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.
[ICCV 2021 Oral] Estimating and Exploiting the Aleatoric Uncertainty in Surface Normal Estimation
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