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 language | English |
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| Title of host publication | 33rd Conference on Neural Information Processing Systems |
| Subtitle of host publication | NeurIPS 2019 |
| Publisher | Neural Information Processing Systems Foundation |
| Publication status | Published - 2019 |
| MoE publication type | A4 Article in a conference publication |
| Event | Conference on Neural Information Processing Systems - Vancouver, Canada Duration: 8 Dec 2019 → 14 Dec 2019 Conference number: 33 https://neurips.cc |
Publication series
| Name | Advances in Neural Information Processing Systems |
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| Publisher | Neural Information Processing Systems Foundation |
| Volume | 32 |
| ISSN (Electronic) | 1049-5258 |
Conference
| Conference | Conference on Neural Information Processing Systems |
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| Abbreviated title | NeurIPS |
| Country | Canada |
| City | Vancouver |
| Period | 08/12/2019 → 14/12/2019 |
| Internet address |