Neural Stochastic Differential Equations: Deep Latent Gaussian Models in the Diffusion Limit

23 May 2019Belinda TzenMaxim Raginsky

In deep latent Gaussian models, the latent variable is generated by a time-inhomogeneous Markov chain, where at each time step we pass the current state through a parametric nonlinear map, such as a feedforward neural net, and add a small independent Gaussian perturbation. This work considers the diffusion limit of such models, where the number of layers tends to infinity, while the step size and the noise variance tend to zero... (read more)

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