Sensitivity Analysis Library in Python (Numpy). Contains Sobol, Morris, Fractional Factorial and FAST methods.

(ICCV 2019) Uncertainty-aware Face Representation and Recognition

[ICCV 2019] Official implementation of "MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation" in PyTorch + Social Distancing

Self-Supervised Learning for OOD Detection (NeurIPS 2019)

The DESPOT online POMDP solver

"What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?", NIPS 2017 (unofficial code).

My implementation of the paper "Simple and Scalable Predictive Uncertainty estimation using Deep Ensembles"

Implementation and evaluation of different approaches to get uncertainty in neural networks

Bayesian dessert for Lasagne

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

Visualizations of distributions and uncertainty

Density estimation using Gaussian mixtures in the presence of noisy, heterogeneous and incomplete data

PyTorch Implementation of QuickNAT and Bayesian QuickNAT, a fast brain MRI segmentation framework with segmentation Quality control using structure-wise uncertainty

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

Official pytorch implementation of the paper "Deep Kernel Transfer in Gaussian Processes for Few-shot Learning"

Some notebooks

Multi Task Learning Implementation with Homoscedastic Uncertainty in Tensorflow

[ICCV'19] Calibration Wizard: A Guidance System for Camera Calibration Based on Modelling Geometric and Corner Uncertainty

Uncertainty Propagation for R Vectors

Airport Surface Simulator and Evaluation Tool 2

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

implementing "recurrent attentive neural processes" to forecast power usage (w. LSTM baseline, MCDropout)

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

Belief functions theory (Dempster-Shafer theory) implementation in C++

Survival analysis in health economic evaluation Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective.

A random forest

Artificial Intelligence Course - Summaries, Exams, Minitests and Comic Sans

Sklearn-compatible interpretability implementations and demos. Accompanying slides: https://bit.ly/2xxA2Lx

tools for dealing with physical quantities: uncertainty propagation and unit conversion