
JAX: A Machine Learning Research Library
Registration
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Details
JAX had its initial open-source release in December 2018 (https://github.com/google/jax). It is currently being used by several groups of researchers for a wide range of advanced applications, from studying spectra of neural networks, to probabilistic programming and Monte Carlo methods, and scientific applications in physics and biology. Users appreciate JAX most of all for its ease of use and flexibility.
This talk is an introduction to JAX and a description of some of its technical aspects. It also includes a discussion of current strengths and limitations, and of our plans for the near future, which may be of interest to potential users and contributors.
The talk will end around 5:30 PM to allow for 1-on-1 discussions with Peter.
Session format: Presentation and demo with open Q&A
Knowledge prerequisites: Attendees should have some knowledge of Python
Hardware/software prerequisites: None
Speakers
Peter Hawkins
Google AI
Peter Hawkins works on JAX at Google AI.
Hosted By
Co-hosted with: GradFUTURES
Contact the organizers