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Dependent Types for Machine Learning in Dex - David Duvenaud - University of Toronto
David Duvenaud · AIPLANS 2021

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.

Author Information

David Duvenaud (University of Toronto)

David Duvenaud is an assistant professor in computer science at the University of Toronto. His research focuses on continuous-time models, latent-variable models, and deep learning. His postdoc was done at Harvard University, and his Ph.D. at the University of Cambridge. David also co-founded Invenia, an energy forecasting and trading company.

AIPLANS 2021 (NeurIPS)

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