49 citations · 156 across the 29 of their papers we have counts for
6 papers · 1 filter
Gaussian Process Deep Belief Networks: A Smooth Generative Model of Shape with Uncertainty Propagation
Alessandro Di Martino, Erik Bodin, Carl Henrik Ek +1
The shape of an object is an important characteristic for many vision problems such as segmentation, detection and tracking. Being independent of appearance, it is possible to gene…
Sequence Alignment with Dirichlet Process Mixtures
Ieva Kazlauskaite, Ivan Ustyuzhaninov, Carl Henrik Ek +1
We present a probabilistic model for unsupervised alignment of high-dimensional time-warped sequences based on the Dirichlet Process Mixture Model (DPMM). We follow the approach in…
Data Association with Gaussian Processes
Markus Kaiser, Clemens Otte, Thomas Runkler +1
The data association problem is concerned with separating data coming from different generating processes, for example when data come from different data sources, contain significa…
DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures
Andrew R. Lawrence, Carl Henrik Ek, Neill D. F. Campbell
We present a non-parametric Bayesian latent variable model capable of learning dependency structures across dimensions in a multivariate setting. Our approach is based on flexible…
Gaussian Process Latent Variable Alignment Learning
Ieva Kazlauskaite, Carl Henrik Ek, Neill D. F. Campbell
We present a model that can automatically learn alignments between high-dimensional data in an unsupervised manner. Our proposed method casts alignment learning in a framework wher…
Active Exploration Using Gaussian Random Fields and Gaussian Process Implicit Surfaces
Sergio Caccamo, Yasemin Bekiroglu, Carl Henrik Ek +1
In this work we study the problem of exploring surfaces and building compact 3D representations of the environment surrounding a robot through active perception. We propose an onli…