Speaker
Patrick Kidger
(Cradle.bio)
Description
This talk provides an overview of several libraries in the open-source JAX ecosystem (such as Equinox, Diffrax, Optimistix, ...) In short, we have been building an "autodifferentiable GPU-capable scipy". These libraries offer the foundational core of tools that have made it possible for us to train neural networks (e.g. score-based diffusions for image generation), solve PDEs, and smoothly handle hybridisations of the two (e.g. fit neural ODEs to scientific data). By the end of the talk, the goal is for you to be able to walk away with a slew of new modelling tools, suitable for tackling problems both in ML and in science.
