From Optimization To Probabilistic Programming

Summary

In Fall 2020 and Spring 2021, this was MIT's 18.337J/6.338J: Parallel Computing and Scientific Machine Learning course. This video is a continuation of the previous video, Episode [08x10]. In this video, get a high-level overview of the theory, concepts ... [Marco] Hi, yes, so I'll be presenting some joint work with Vikash on Gen, a Lectures by Professor Eric Hehner cs.utoronto.ca/~hehner/FMSD/ Alex Lew's thesis defense Title: Automatic Integration and Differentiation of "Big Data problems are all over the place at Facebook. We've historically used discriminative methods such as neural networks ... Jules Jacobs (Radboud University Nijmegen) Paper: dl.acm.org/doi/pdf/10.1145/3434339 Abstract Recorded at the ML in PL 2019 Conference, the University of Warsaw, 22-24 November 2019. Martin Jankowiak (Uber AI Labs) ... This talk shows how to make smarter, safer AI that understands the world we do, using a new symbolic medium that I helped ... Vikash Mansinghka, MIT simons.berkeley.edu/talks/vikash-mansinghka-10-06-2016 Uncertainty in Computation. Keynote Title: From Probabilistic Circuits to