Probabilistic programming uses programming language techniques to make it easy to denote and perform inference in the kinds of probabilistic models that inform decision-making, accelerate scientific discovery, and underlie modern attacks on the problem of artificial intelligence. Deep learning uses programming language techniques to automate supervised learning of program parameter values by gradient-based optimization.
What happens if we put them together? This talk will review probabilistic programming. It will also introduce inference compilation and address how linking deep learning and probabilistic programming is leading to powerful new AI techniques while also opening up significant new research questions.