Search Results for author:
Found 5 papers, 1 papers with code
Variational Nearest Neighbor Gaussian Process
Variational approximations to Gaussian processes (GPs) typically use a small set of inducing points to form a low-rank approximation to the covariance matrix.
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations
In this work, we introduce the hierarchical inducing point GP (HIP-GP), a scalable inter-domain GP inference method that enables us to improve the approximation accuracy by increasing the number of inducing points to the millions.
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data
We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is able to uncover nonlinear, smooth latent dynamics from sequential data.
A NOVEL VARIATIONAL FAMILY FOR HIDDEN NON-LINEAR MARKOV MODELS
In the case of sequential data, closed-form inference is possible when the transition and observation functions are linear.
Black box variational inference for state space models
These models have the advantage of learning latent structure both from noisy observations and from the temporal ordering in the data, where it is assumed that meaningful correlation structure exists across time.
