Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Rasmussen, Carl E."'
Publikováno v:
Advances in Neural Information Processing Systems (New Orleans), 2022
The kernel function and its hyperparameters are the central model selection choice in a Gaussian proces (Rasmussen and Williams, 2006). Typically, the hyperparameters of the kernel are chosen by maximising the marginal likelihood, an approach known a
Externí odkaz:
http://arxiv.org/abs/2211.02476
Deep kernel learning (DKL) and related techniques aim to combine the representational power of neural networks with the reliable uncertainty estimates of Gaussian processes. One crucial aspect of these models is an expectation that, because they are
Externí odkaz:
http://arxiv.org/abs/2102.12108
Gaussian Processes (GPs) are powerful non-parametric Bayesian regression models that allow exact posterior inference, but exhibit high computational and memory costs. In order to improve scalability of GPs, approximate posterior inference is frequent
Externí odkaz:
http://arxiv.org/abs/1910.04536
Excellent variational approximations to Gaussian process posteriors have been developed which avoid the $\mathcal{O}\left(N^3\right)$ scaling with dataset size $N$. They reduce the computational cost to $\mathcal{O}\left(NM^2\right)$, with $M\ll N$ b
Externí odkaz:
http://arxiv.org/abs/1903.03571
While Gaussian processes (GPs) are the method of choice for regression tasks, they also come with practical difficulties, as inference cost scales cubic in time and quadratic in memory. In this paper, we introduce a natural and expressive way to tack
Externí odkaz:
http://arxiv.org/abs/1809.04400
Publikováno v:
R. Frigola, Y. Chen and C. E. Rasmussen. Variational Gaussian Process State-Space Models, in Advances in Neural Information Processing Systems (NIPS), 2014
State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian proces
Externí odkaz:
http://arxiv.org/abs/1406.4905
Gaussian processes (GPs) are a powerful tool for probabilistic inference over functions. They have been applied to both regression and non-linear dimensionality reduction, and offer desirable properties such as uncertainty estimates, robustness to ov
Externí odkaz:
http://arxiv.org/abs/1402.1389
Gaussian process state-space models (GP-SSMs) are a very flexible family of models of nonlinear dynamical systems. They comprise a Bayesian nonparametric representation of the dynamics of the system and additional (hyper-)parameters governing the pro
Externí odkaz:
http://arxiv.org/abs/1312.4852
Publikováno v:
Published in NIPS 2013, Advances in Neural Information Processing Systems 26, pp. 3156--3164
State-space models are successfully used in many areas of science, engineering and economics to model time series and dynamical systems. We present a fully Bayesian approach to inference \emph{and learning} (i.e. state estimation and system identific
Externí odkaz:
http://arxiv.org/abs/1306.2861
Publikováno v:
In IFAC Proceedings Volumes 2014 47(3):4097-4102
