The model is capable of learning an effective latent representation and generating novel data of one view given data of the other view. We evaluate our model on ...
In this paper, we propose the multi-view collaborative Gaussian process dynamical systems (McGPDSs) model, which makes full use of the characteristics of multi- ...
Mar 6, 2024 · In this paper, we propose the multi-view collaborative Gaussian process dynamical systems (McGPDSs) model, which assumes that the private latent ...
This paper proposes the multi-view collaborative Gaussian process dynamical systems (McGPDSs) model, which assumes that the private latent variable for each ...
In this paper, we propose the multi-view collaborative Gaussian process dynamical systems (McGPDSs) model, which makes full use of the characteristics of multi- ...
We introduce the collaborative multi-output. Gaussian process (GP) model for learning dependent tasks with very large datasets.
Jun 9, 2019 · In order to better model high-dimensional sequential data, we propose a collaborative multi-output Gaussian process dynamical system (CGPDS), ...
In this work, we propose a multi-task, multi-kernel model consisting of Gaussian process dynamical system factors that we shall dub from the Multi-GPDS. We form ...
Abstract. This paper presents a dependent multi-output Gaussian process (GP) for modeling complex dynamical systems. The outputs are dependent in this model ...
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An extension of the heterogeneous multioutput Gaussian processes model, where its latent functions are drawn from convolution processes, is introduced, ...