Accession Number:

AD1067827

Title:

Amortized Inference for Probabilistic Programs

Descriptive Note:

Technical Report,01 Nov 2013,31 Aug 2018

Corporate Author:

Stanford University Stanford United States

Personal Author(s):

Report Date:

2019-02-01

Pagination or Media Count:

17.0

Abstract:

Probabilistic programming holds the promise of revolutionizing computational systems by enabling non-experts to embed sophisticated probabilistic AI machine learning, natural language processing, and computer vision. Stanford set out to radically accelerate probabilistic programming systems by targeting the full implementation stack from inference algorithms to hardware. They have made significant advances in inference algorithms, compilation techniques, and applications. Many of these advances have been released as open source software andor transitioned to open source projects carried on by industry partners. This has contributed to major growth in the probabilistic programming community in both academia and industry. They expect in the near future to see further growth and uses in high-value applications across diverse sectors.

Subject Categories:

  • Cybernetics

Distribution Statement:

APPROVED FOR PUBLIC RELEASE