General Purpose Probabilistic Programming Platform with Effective Stochastic Inference
Abstract
Probabilistic modeling and machine learning have proven to be powerful tools in many defense, industrial, and scientific computing applications. Unfortunately, their continuing adoption has been hindered because engineering with them requires PhD-level expertise. Our research in this program led to the creation of multiple open-source probabilistic programming languages. These languages achieved key program goals, such as (i) reducing the lines of code required to build state-of-the-art machine learning systems by ~50x; (ii) making machine learning and data science capabilities accessible to a broader class of programmers, by providing automatic model discovery mechanisms and simple, SQL like query languages; (iii) making it possible to deploy rich generative models to solve applied problems, and thereby solve hard 3D computer vision problems with no training data; and (iv) revealing interfaces and abstractions that unify abroad set of probabilistic programming languages and enable multiple inference strategies or ``solvers'' to interoperate
Document Details
- Document Type
- Technical Report
- Publication Date
- Apr 01, 2018
- Accession Number
- AD1050972
Entities
People
- Vikash Mansinghka
Organizations
- Massachusetts Institute of Technology