NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:7407
Title:ODE2VAE: Deep generative second order ODEs with Bayesian neural networks

This paper combine several modeling ingredients (BNNs, ODEs, and VAEs) to produce a new family of models. It's not clear to my whether adding second-order dynamics in particular is advantageous over just adding extra latent dimensions to the state, which I think would be a generalization of the current approach. However, seeing a comparison against GPLVM-based models was nice, since these two approaches represent very different technical approaches to the same problem.