Dependent Types for Machine Learning in Dex - David Duvenaud - University of Toronto

This talk will give a gentle introduction to Dex, an experimental programming language. Dex is designed to combine the clarity and safety of high-level functional languages with the efficiency of low-level numerical languages. For example, Dex allows one to move much of the informal type and shape information normally contained in comments into compile-time checked types, while also omitting unambiguous details, to keep things terse. It also allows in-place updates and stateful, loopy code that can automatically take advantage of parallelism in a fine-grained way. We'll demonstrate these features on standard deep architectures like attention and graph neural nets.