Assessing a Variational Autoencoder on MNIST using Pytorch

Learn how to visualize the latent space and generate data using a VAE in Pytorch.

Minimalist Variational Autoencoder in Pytorch with CUDA GPU

Variational Autoencoders in Pytorch with CUDA GPU

Variational Auto-Encoders and the Expectation-Maximization Algorithm

Relationship between Variational Autoencoders (VAE) and the Expectation Maximization Algorithm. Simple Explanation

Towards SMC: Using the Dirac-delta function in Sampling and Sequential Monte Carlo

We derive the Dirac-delta function, explain how to use to approximate an Empirical PDF for a sample.

Towards SMC: Sequential Importance Sampling

Sequential Importance Sampling intuition simply explained for SMC

Towards SMC: Importance Sampling with Sequential Data

Importance Sampling tutorial for Sequential Data

Towards SMC: Importance Sampling Explained

Importance Sampling as a first step towards Sequential Monte Carlo (SMC)

Gaussian Expectation Propagation

Description of Expectation Propagation using Multivariate Normal factors for the global approximation.

Multivariate Normal as an Exponential Family Distribution

How to rewrite a Multivariate Normal distribution as a member of the Exponential Family.

Expectation Propagation - The Essential Minimum

The bare practical minimum to understand Expectation Propagation