ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

12 Citations (Scopus)

Abstract

We present Ordinary Differential Equation Variational Auto-Encoder (ODE2VAE), a latent second order ODE model for high-dimensional sequential data. Leveraging the advances in deep generative models, ODE2VAE can simultaneously learn the embedding of high dimensional trajectories and infer arbitrarily complex continuous-time latent dynamics. Our model explicitly decomposes the latent space into momentum and position components and solves a second order ODE system, which is in contrast to recurrent neural network (RNN) based time series models and recently proposed black-box ODE techniques. In order to account for uncertainty, we propose probabilistic latent ODE dynamics parameterized by deep Bayesian neural networks. We demonstrate our approach on motion capture, image rotation and bouncing balls datasets. We achieve state-of-the-art performance in long term motion prediction and imputation tasks.
Original languageEnglish
Title of host publication33rd Conference on Neural Information Processing Systems
Subtitle of host publicationNeurIPS 2019
PublisherNeural Information Processing Systems Foundation
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventConference on Neural Information Processing Systems - Vancouver, Canada
Duration: 8 Dec 201914 Dec 2019
Conference number: 33
https://neurips.cc

Publication series

NameAdvances in Neural Information Processing Systems
PublisherNeural Information Processing Systems Foundation
Volume32
ISSN (Electronic)1049-5258

Conference

ConferenceConference on Neural Information Processing Systems
Abbreviated titleNeurIPS
CountryCanada
CityVancouver
Period08/12/201914/12/2019
Internet address

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