Implicit Reparametrization Trick

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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

We plan to implement the following distributions in our library:

  • Gaussian normal distribution

  • Dirichlet distribution (Beta distributions)

  • Sampling from a mixture of distributions

  • Sampling from the Student’s t-distribution

  • Sampling from an arbitrary factorized distribution

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

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
params = Encoder(inputs)
gauss = irt.Normal(*params)
deviated = gauss.rsample()
outputs = Decoder(deviated)

In this example, we demonstrate the use of a mixture of distributions using our library.:

import irt
params = Encoder(inputs)
mix = irt.Mixture([irt.Normal(*params), irt.Dirichlet(*params)])
deviated = mix.rsample()
outputs = Decoder(deviated)