Deep Probabilistic Programming Languages: A Qualitative Study
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.
Code (0)
등록된 구현이 없습니다.
Tasks
BIG-bench Machine LearningProbabilistic ProgrammingSimilar Papers 제목 키워드 기반
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that cap…
Bayesian InferenceProbabilistic ProgrammingTime SeriesTime Series AnalysisDeployable probabilistic programming
We propose design guidelines for a probabilistic programming facility suitable for deployment as a part of a production software system. As a reference implementation, we introduce Infergo, a probabilistic programming fa…
Decision MakingProbabilistic ProgrammingAutomated learning with a probabilistic programming language: Birch
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We c…
FormMultiple Object TrackingObject TrackingProbabilistic ProgrammingA New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo. Our approach is simple to implement and easy to parallelize. It appli…
Probabilistic ProgrammingAutomatic Generation of Probabilistic Programming from Time Series Data
Probabilistic programming languages represent complex data with intermingled models in a few lines of code. Efficient inference algorithms in probabilistic programming languages make possible to build unified frameworks …
DescriptiveProbabilistic ProgrammingregressionTime Series+1