- Large|scale variational Gaussian state|space models🔍
- Large|scale nonlinear Gaussian state|space modeling🔍
- catniplab/xfads🔍
- Learning spatial patterns with variational Gaussian processes🔍
- Fast Variational Learning in State|Space Gaussian Process Models🔍
- Gaussian Variational Approximations for High|dimensional State ...🔍
- Scalable Variational Gaussian Process Classification🔍
- Variational Gaussian Process State|Space Models🔍
Large|scale variational Gaussian state|space models
Large-scale variational Gaussian state-space models - arXiv
We introduced a structured variational approximation and corresponding inference algorithm that reduce the complexity of inference in state- ...
Large-scale nonlinear Gaussian state-space modeling - arXiv
We introduce a low-rank structured variational autoencoding framework for nonlinear Gaussian state-space graphical models capable of capturing dense covariance ...
catniplab/xfads: large scale variational Gaussian state-space models
Search code, repositories, users, issues, pull requests... · Provide feedback · Saved searches · catniplab/xfads · eXponential FAmily Dynamical Systems (XFADS): ...
Learning spatial patterns with variational Gaussian processes
A variational Gaussian process (VGP) model specialized in spatial data is introduced, capitalizing on recent advances in the machine learning field.
Fast Variational Learning in State-Space Gaussian Process Models
Abstract: Gaussian process (GP) regression with 1D inputs can often be performed in linear time via a stochastic differential equation formulation.
Gaussian Variational Approximations for High-dimensional State ...
We consider a Gaussian variational approximation of the posterior density in high-dimensional state space models. ... model parameters is large. We ...
Scalable Variational Gaussian Process Classification
We show how to scale the model within a variational inducing point framework, out-performing the state of the art on benchmark datasets.
Variational Gaussian Process State-Space Models - NIPS
large external load of unknown weight and aerodynamic characteristics. A model of the nonlinear dynamics of the new system can be very useful in order to ...
Free-Form Variational Inference for Gaussian Process State-Space...
Gaussian process state-space models (GPSSMs) provide a principled and flexible approach to modeling the dynamics of a latent state, ...
Fast variational learning in state-space Gaussian process models
Video presentation for the paper: Paul E. Chang, William J. Wilkinson, Mohammad Emtiyaz Khan, and Arno Solin (2020). Fast variational ...
Understanding Local Linearisation in Variational Gaussian Process ...
We describe variational inference approaches in. Gaussian process state space models in terms of local linearisations of the approximate posterior function.
Gaussian variational approximation for high-dimensional state ...
Our article considers a Gaussian variational approximation of the posterior density in a high-dimensional state space model. The variational parameters to ...
Gaussian Process State-Space Models with Time-Varying ...
We propose time-varying Gaussian process state-space models (TVGPSSM) whose hyper-parameters vary with time.
Variational Gaussian Process State-Space Models. - YouTube
State-space models have been successfully used for more than fifty years in different areas of science and engineering.
(PDF) Variational Gaussian Process State-Space Models
State-space models have been successfully used for more than fifty years in different areas of science and engineering.
Bayesian estimation of large-scale simulation models with Gaussian ...
This is provided by a Linear Model of Coregionalization, where each latent variable is a sparse variational Gaussian ... Likelihood-free inference in state-space ...
Fast Variational Bayesian Linear State-Space Model - SpringerLink
This paper presents a fast variational Bayesian method for linear state-space models. The standard variational Bayesian expectation-maximization (VB-EM) ...
[PDF] Scalable Variational Gaussian Process Classification
This work shows how to scale the model within a variational inducing point framework, outperforming the state of the art on benchmark datasets, ...
Variational Inference for Gaussian Process Models with Linear ...
Large-scale Gaussian process inference has long faced practical challenges due to time and space complexity that is superlinear in dataset size.
Gaussian Variational Approximations for High-dimensional State ...
It is evident that using this large scale model for forecasting future values is associated with a large uncertainty. ... variational Bayes in state space models: ...