Inference Over Programs That Make Predictions

Abstract

This abstract extends on the previous work (arXiv:1407.2646, arXiv:1606.00075) on program induction using probabilistic programming. It describes possible further steps to extend that work, such that, ultimately, automatic probabilistic program synthesis can generalise over any reasonable set of inputs and outputs, in particular in regard to text, image and video data.

arXiv commentThe International Conference on Probabilistic Programming, 2018

References in corpus (7)

• Efficient Estimation of Word Representations in Vector Space
• A Compilation Target for Probabilistic Programming Languages
• Inducing Probabilistic Programs by Bayesian Program Merging
• Structured Priors for Structure Learning
• A Universal Marginalizer for Amortized Inference in Generative Models
• Data-driven Sequential Monte Carlo in Probabilistic Programming
• Nesting Probabilistic Programs