Abstract: Recently, there has been a growing interest in solving ordinary differential equation (ODE) systems using function approximators, e.g., Gaussian processes or neural networks. Such models are proven useful for modeling continuous-time dynamic phenomena and also interestingly connected with very deep networks with skip connection, flow-based deep generative models and reinforcement learning.
In this talk, I’ll present an overview of our NeurIPS paper from last December: ODE2VAE, a latent second-order ODE model for high-dimensional sequential data. ODE2VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. In particular, the talk will focus on the advantages of ODE modeling over discrete counterparts, why latent modeling is useful and the benefits of being Bayesian in this setting. To complete the picture, I’ll briefly give the historical context, draw connections with related techniques, and discuss exciting future directions.

Speaker: Yildiz Cagatay

Affiliation: Aalto University, Department of Computer Science

Place of Seminar: Lecture Hall T6, Konemiehentie 2, Aalto University