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  1. A tutorial on "Bayesian Compression for Deep Learning" published at NIPS (2017).

    Python 179 43

  2. A tutorial on 'Soft weight-sharing for Neural Network compression' published at ICLR2017

    Jupyter Notebook 140 30

  3. This code accompanies "Differentiable probabilistic models of scientific imaging with the Fourier slice theorem", UAI 2019

    Jupyter Notebook 15 4

  4. A minimal implementation of a VAE with BinConcrete (relaxed Bernoulli) latent distribution in TensorFlow.

    Python 12 1

  5. Turning float tensors to binary tensors according to IEEE-754 standard.

    Python 6 2

  6. Forked from JasperSnoek/spearmint

    Spearmint uses Gaussian Processes to automatically optimize hyper parameter. This is a fork of Spearmint for the deep learning community. More specifically, it provides support for Theano users.

    Python 12 1

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September 2020

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