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  1. A multi-task learning example for the paper https://arxiv.org/abs/1705.07115

    Jupyter Notebook 435 106

  2. Code for the paper "A Theoretically Grounded Application of Dropout in Recurrent Neural Networks"

    Lua 349 76

  3. Experiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"

    Python 335 94

  4. Code for Concrete Dropout as presented in https://arxiv.org/abs/1705.07832

    Jupyter Notebook 197 59

  5. Dropout As A Bayesian Approximation: Code

    Shell 167 38

  6. Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.

    JavaScript 96 31

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August - September 2020

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July 2020

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