Analysis of ODE2VAE with Examples

Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE) is a deep latent variable model that aims to learn complex distributions over high-dimensional sequential data and their low-dimensional representations in a hierarchical latentspace. The hierarchical organization of the latent space embeds a physics-guided inductive bias in the model. In this paper, we analyze the latent representations inferred by the ODE2VAE model over three different physical motion datasets:bouncing balls, projectile motion, and simple pendulum. We show that the model is able to learn meaningful latent representations to an extent without any supervision.

In Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021)