Probabilistic Machine Learning for Finance and Investing: A Primer to Generative AI with Python

Code for the paper "Getting a CLUE: A Method for Explaining Uncertainty Estimates"

Machine learning models for estimating aleatoric and epistemic uncertainty with evidential and ensemble methods.

NOMU: Neural Optimization-based Model Uncertainty

This repository contains a demontstration of how to build, train and evaluate a neural network capable of measuring epistemic uncertainty as proposed by the authors of Evidential Deep Learning to Quantify Classification Uncertainty

Energy production forecasting ⚡ with PoC of Bayesian Neural Network 🎲

Uncertainty quantification and out-of-distribution detection using surjective normalizing flows

Work as part of ANL summer 2020 research on uncertainity quanitification methods in graph neural networks

Original implementation of the EMD (empirical model discrepancy) model comparison criterion

the prior distribution for probabilistic numerical methods

Code for the ICASSP'19 submission "Modelling Sample Informativeness for Deep Affective Computing".

Generalized Automatic Pipeline for inspecting and fixing uncertainties in your data