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Pinned

  1. forward and reverse mode automatic differentiation primitives for Julia Base + StdLibs

    Julia 301 65

  2. AD-backend agnostic system defining custom forward and reverse mode rules. This is the light weight core to allow you to define rules for your functions in your packages, without depending on any p…

    Julia 162 38

  3. Utilities for testing custom AD primitives.

    Julia 40 13

  4. MINE Public

    Mutual Information Neural Estimator implemented in Tensorflow

    Jupyter Notebook 42 15

  5. autothesis Public

    automate thesis writing

    Python

  6. FairML Public

    Fairness in ML using adversarial training.

    Jupyter Notebook 4

626 contributions in the last year

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Contribution activity

June 2022

Created a pull request in FluxML/Zygote.jl that received 7 comments

deprecate dropgrad and ignore

Contributes to #1235 I'll do nograd separately since it is used for a few adjoints that may need to be ported first.

+70 −55 7 comments
Opened 5 other pull requests in 2 repositories
Reviewed 2 pull requests in 2 repositories
invenia/DateSelectors.jl 1 pull request
JuliaDiff/ChainRules.jl 1 pull request
Opened 5 issues in 3 repositories