This document introduces the t3-Variational Autoencoder (ICLR 2023), which uses Student’s t-distributions to model heavy-tailed data distributions and improve latent variable representations. It also explores the framework of Information Geometry, focusing on how generative models can be understood through statistical manifolds, divergences, and Riemannian metrics, providing a deeper understanding of probability distributions and their applications in machine learning, signal processing, and neuroscience.