Treffer: Pyro: Deep Universal Probabilistic Programming.

Title:
Pyro: Deep Universal Probabilistic Programming.
Authors:
Bingham, Eli1 ELI.BINGHAM@UBER.COM, Chen, Jonathan P.1 JPCHEN@UBER.COM, Jankowiak, Martin1 JANKOWIAK@UBER.COM, Obermeyer, Fritz1 FRITZO@UBER.COM, Pradhan, Neeraj1 NPRADHAN@UBER.COM, Karaletsos, Theofanis1 THEOFANIS@UBER.COM, Singh, Rohit1 ROHITS@UBER.COM, Szerlip, Paul1 PAS@UBER.COM, Horsfall, Paul2 HORSFALLP@GMAIL.COM, Goodman, Noah D.2 NGOODMAN@STANFORD.EDU
Source:
Journal of Machine Learning Research. 2019, Vol. 20 Issue 2-29, p1-6. 6p.
Subject Terms:
*PROGRAMMING languages, *PROBABILITY theory, *INFERENTIAL statistics, *STOCHASTIC analysis, *DEEP learning
Database:
Academic Search Index

Weitere Informationen

Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large data sets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch, a modern GPU-accelerated deep learning framework. To accommodate complex or model-specific algorithmic behavior, Pyro leverages Poutine, a library of composable building blocks for modifying the behavior of probabilistic programs. [ABSTRACT FROM AUTHOR]