HackerPapers

arXiv:1804.06458cs.AI

Deep Probabilistic Programming Languages: A Qualitative Study

Guillaume Baudart, Martin Hirzel, Louis Mandel

HN submission. Found in a submission link or body. Submitters do not count as commenters in the main ranking; a submission is not an endorsement.

Abstract

Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages. If successful, this would be a big step forward in machine learning and programming languages. Unfortunately, as of now, this new crop of languages is hard to use and understand. This paper addresses this problem directly by explaining deep probabilistic programming languages and indirectly by characterizing their current strengths and weaknesses.

arXiv comments
Journal reference
DOI
Subjects
cs.AIArtificial Intelligence cs.PLProgramming Languages

On Hacker News

No comments linking this paper were found in the collected sources. Found in 1 HN submission from Apr 2018 to Apr 2018. Open submission discussions.

Comments linking this paper, per year.

Discussed in comments0 threads

No comments linking this paper were found in the collected sources. Open submission discussions.

Submitted to Hacker News

Connected through shared HN threads, not inferred similarity or a recommendation.