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cs.LG2023
Bayesian Gaussian Process ODEs via Double Normalizing Flows
Jian Xu, Shian Du, Junmei Yang +3
Recently, Gaussian processes have been used to model the vector field of continuous dynamical systems, referred to as GPODEs, which are characterized by a probabilistic ODE equatio…
cs.LG2023
Bayesian Beta-Bernoulli Process Sparse Coding with Deep Neural Networks
Arunesh Mittal, Kai Yang, Paul Sajda +1
Several approximate inference methods have been proposed for deep discrete latent variable models. However, non-parametric methods which have previously been successfully employed…
cs.LG2023
Nonlinear Kalman Filtering with Reparametrization Gradients
San Gultekin, Brendan Kitts, Aaron Flores +1
We introduce a novel nonlinear Kalman filter that utilizes reparametrization gradients. The widely used parametric approximation is based on a jointly Gaussian assumption of the st…