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Found 7 papers, 5 papers with code
Memory-Based Dual Gaussian Processes for Sequential Learning
Sequential learning with Gaussian processes (GPs) is challenging when access to past data is limited, for example, in continual and active learning.
Variational Gaussian Process Diffusion Processes
Diffusion processes are a class of stochastic differential equations (SDEs) providing a rich family of expressive models that arise naturally in dynamic modelling tasks.
PriorCVAE: scalable MCMC parameter inference with Bayesian deep generative modelling
Recent advances have shown that GP priors, or their finite realisations, can be encoded using deep generative models such as variational autoencoders (VAEs).
Fantasizing with Dual GPs in Bayesian Optimization and Active Learning
Gaussian processes (GPs) are the main surrogate functions used for sequential modelling such as Bayesian Optimization and Active Learning.
Scalable Inference in SDEs by Direct Matching of the Fokker-Planck-Kolmogorov Equation
Simulation-based techniques such as variants of stochastic Runge-Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning.
Sparse Gaussian Processes for Stochastic Differential Equations
We frame the problem of learning stochastic differential equations (SDEs) from noisy observations as an inference problem and aim to maximize the marginal likelihood of the observations in a joint model of the latent paths and the noisy observations.
Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation
Simulation-based techniques such as variants of stochastic Runge–Kutta are the de facto approach for inference with stochastic differential equations (SDEs) in machine learning.
