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Pinned

  1. A curated list of awesome machine learning interpretability resources.

    2.5k 458

  2. Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

    Jupyter Notebook 589 189

  3. Materials for GWU DNSC 6279 and DNSC 6290.

    Jupyter Notebook 222 170

  4. Practical ideas on securing machine learning models

    TeX 29 6

  5. kdd_2019 Public

    Paper and talk from KDD 2019 XAI Workshop

    TeX 19 5

  6. GWU_rml Public

    Jupyter Notebook 21 14

249 contributions in the last year

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Contribution activity

February 2022

Created 8 commits in 1 repository

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