Sequential Latent Variable Models for Few-Shot High-Dimensional Time-Series Forecasting

Kalman variational auto-encoders (Fraccaro et al., 2017) , that can learn from data disentangled and more interpretable visual and dynamic representations. Finally, we will show that to deal with temporal applications that require a high memory capacity we can combine deep latent variable models with external memory architectures, as in the generative temporal model with spatial memory of Fraccaro et al., (2018) .