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27 changes: 16 additions & 11 deletions README.md
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@@ -1,9 +1,19 @@
# Implicit Reparametrization Trick
<div align="center">
<h1> Implicit Reparametrization Trick </h1>
</div>

<div align="center">
<img src="images/implicit.webp" width="500px" />
</div>

<p align="center">
<a href="https://github.com/intsystems/discrete-variables-relaxation/tree/main/tests">
<img alt="Coverage_2" src="https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/testing.yml/badge.svg" />
</a>
<a href="https://intsystems.github.io/discrete-variables-relaxation">
<img alt="Docs" src="https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/docs.yml/badge.svg" />
</a>
</p>

<table>
<tr>
Expand All @@ -20,16 +30,11 @@
</tr>
</table>


![Testing](https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/testing.yml/badge.svg)
![Docs](https://github.com/intsystems/implicit-reparameterization-trick/actions/workflows/docs.yml/badge.svg)


## Description
## 💡 Description

This repository implements an educational project for the Bayesian Multimodeling course. It implements algorithms for sampling from various distributions, using the implicit reparameterization trick.

## Scope
## 🗃 Scope
We plan to implement the following distributions in our library:
- [x] Gaussian normal distribution (*)
- [x] Dirichlet distribution (Beta distributions)(\*)
Expand All @@ -44,11 +49,11 @@ We plan to implement the following distributions in our library:

(\*\*\*) - this distribution is not very clear in implementation, its inclusion is questionable

## Stack
## 📚 Stack

We plan to inherit from the torch.distribution.Distribution class, so we need to implement all the methods that are present in that class.

## Usage
## 👨‍💻 Usage
In this example, we demonstrate the application of our library using a Variational Autoencoder (VAE) model, where the latent layer is modified by a normal distribution.
```
>>> import torch.distributions.implicit as irt
Expand All @@ -66,7 +71,7 @@ In this example, we demonstrate the use of a mixture of distributions using our
>>> outputs = Decoder(deviated)
```

## Links
## 📬 Links
- [LinkReview](https://github.com/intsystems/implitic-reparametrization-trick/blob/main/linkreview.md)
- [Plan of project](https://github.com/intsystems/implitic-reparametrization-trick/blob/main/planning.md)
- [BlogPost](blogpost/Blog_post_sketch.pdf)
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