Abstract: In this talk, we will learn about Pyro (http://pyro.ai) a PPL built on PyTorch. We will discuss what probabilistic programming is, and how we can integrate it with deep learning to tackle open machine learning problems in generative modeling. We will talk about approximate inference techniques such as variational inference, and walk through some of the tools and examples to make inference on models automatic. If you are a data scientist, an ML engineer, or an ML researcher, this talk will be of interest to you!
Bio: JP is a research scientist at Facebook where he works on probabilistic programming, approximate inference, and Bayesian nonparametrics. He is a founding coauthor of the probabilistic programming language Pyro. The main question that guides his research is: how do we build and perform inference on models in an automatic yet principled way? Prior to Facebook, he was at Uber AI Labs working at the intersection of deep learning and statistics, focusing on time series forecasting and mapping for self driving cars.