DeeProb-kit is a unified library written in Python consisting of a collection of deep probabilistic models (DPMs) that are tractable and exact representations for the modelled probability distributions. The availability of a representative selection of DPMs in a single library makes it possible to combine them in a straightforward manner, a common practice in deep learning research nowadays. In addition, it includes efficiently implemented learning techniques, inference routines, statistical algorithms, and provides high-quality fully-documented APIs. The development of DeeProb-kit will help the community to accelerate research on DPMs as well as to standardise their evaluation and better understand how they are related based on their expressivity.
@article{arxiv.2212.04403,
title = {DeeProb-kit: a Python Library for Deep Probabilistic Modelling},
author = {Lorenzo Loconte and Gennaro Gala},
journal= {arXiv preprint arXiv:2212.04403},
year = {2022}
}