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Computer Science > Machine Learning

arXiv:2212.04403 (cs)
[Submitted on 8 Dec 2022]

Title:DeeProb-kit: a Python Library for Deep Probabilistic Modelling

Authors:Lorenzo Loconte, Gennaro Gala
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Abstract: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.
Subjects: Machine Learning (cs.LG)
Cite as: arXiv:2212.04403 [cs.LG]
  (or arXiv:2212.04403v1 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2212.04403

Submission history

From: Lorenzo Loconte [view email]
[v1] Thu, 8 Dec 2022 17:02:16 UTC (29 KB)
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