| Title | Implicit Reparametrization Trick for BMM |
| Authors | Matvei Kreinin, Maria Nikitina, Petr Babkin, Iryna Zabarianska |
| Consultant | Oleg Bakhteev, PhD |
This repository implements an educational project for the Bayesian Multimodeling course. It implements algorithms for sampling from various distributions, using the implicit reparameterization trick.
We plan to implement the following distributions in our library:
- Gaussian normal distribution (*)
- Dirichlet distribution (Beta distributions)(*)
- Mixture of the same family distributions (**)
- Student's t-distribution (**) (*)
- VonMises distribution (***)
- Sampling from an arbitrary factorized distribution (***)
(*) - this distribution is already implemented in torch using the explicit reparameterization trick, we will implement it for comparison
(**) - this distribution is added as a backup, their inclusion is questionable
(***) - this distribution is not very clear in implementation, its inclusion is questionable
We plan to inherit from the torch.distribution.Distribution class, so we need to implement all the methods that are present in that class.
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)
