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Motivation

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For lots of mathematical problems we need an ability to sample discrete random variables. -The problem is that due to continuous nature of deep learning optimization, the usage of truly discrete random variables is infeasible. -Thus we use different relaxation methods. -One of them, Concrete distribution or Gumbel-softmax (this is one distribution proposed in parallel by two research groups) is implemented in different DL packages. -In this project we implement different alternatives to it.

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<img src="assets/logo.svg" width="200px" />
+<h1> Just Relax It </h1>
+Discrete variables relaxation
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πŸ“¬ Assets

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  1. Technichal Meeting 1 - +Presentation

  2. +
  3. Technichal Meeting 2 - Jupyter +Notebook

  4. +
  5. Blog +Post

  6. +
  7. Documentation

  8. +
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+

πŸ’‘ Motivation

+

For lots of mathematical problems we need an ability to sample discrete +random variables. The problem is that due to continuos nature of deep +learning optimization, the usage of truely discrete random variables is +infeasible. Thus we use different relaxation method. One of them, +Concrete distribution or +Gumbel-softmax (this is one +distribution proposed in parallel by two research groups) is implemented +in different DL packages. In this project we implement different +alternatives to it.

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+
<img src="assets/overview.png"/>
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Algorithms to implement (from simplest to hardest)

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πŸ—ƒ Algorithms to implement (from simplest to hardest)

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Problem details

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To make the library consistent, we integrate imports of distributions from pyro and pytorch into the library, so that all the categorical distributions can be imported from one entrypoint.

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🧩 Problem details

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To make to library constistent, we integrate imports of distributions +from pyro and pytorch into the library, so that all the categorical +distributions can be imported from one entrypoint.

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Contributors

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πŸ‘₯ Contributors

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