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patrick-kidger/README.md

JAX scientific ML ecosystem:

Probably the reason you're here. I would highlight:

  1. Equinox: elegant neural networks. GitHub Repo stars

  2. Diffrax: numerical ODE/SDE solvers. GitHub Repo stars

  3. jaxtyping: shape/dtype annotations for arrays. GitHub Repo stars

  4. Lineax: linear/least-squares solvers. GitHub Repo stars

  5. Optimistix: root finding, least squares, etc. GitHub Repo stars

Other links:

Me:

I currently wear multiple hats across bio/ML/CS at Cradle.bio. These days I am generally interested in scientific ML, and specifically the application of ML to unsolved problems in biology!

I also hold an honorary lectureship at Imperial College London. In past lives I previously wore the same multitude of hats at Google X, and did my PhD at the University of Oxford.

Pinned Loading

  1. google-research/torchsde google-research/torchsde Public

    Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

    Python 1.6k 201

  2. torchtyping torchtyping Public

    Type annotations and dynamic checking for a tensor's shape, dtype, names, etc.

    Python 1.4k 34

  3. equinox equinox Public

    Elegant easy-to-use neural networks + scientific computing in JAX. https://docs.kidger.site/equinox/

    Python 2.1k 142

  4. diffrax diffrax Public

    Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

    Python 1.5k 133

  5. jaxtyping jaxtyping Public

    Type annotations and runtime checking for shape and dtype of JAX/NumPy/PyTorch/etc. arrays. https://docs.kidger.site/jaxtyping/

    Python 1.2k 63

  6. NeuralCDE NeuralCDE Public

    Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)

    Python 630 70