Deep Probabilistic Programming Languages: A Qualitative Study
Authors: Guillaume Baudart, Martin Hirzel, Louis Mandel
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.
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