We marry ideas from deep neural networks and approximate Bayesian inference to derive a
generalised class of deep, directed generative models, endowed with a new algorithm for …

The advancements in genomics and biomedical technologies have generated vast amounts
of biological and physiological data, which present opportunities for understanding human …

A fundamental problem in neural network research, as well as in many other disciplines, is
finding a suitable representation of multivariate data, ie random vectors. For reasons of …

In this paper, we review recent advances in blind source separation (BSS) and independent
component analysis (ICA) for nonlinear mixing models. After a general introduction to BSS …

Lithology identification based on conventional well-logging data is of great importance for
geologic features characterization and reservoir quality evaluation in the exploration and …

Nonlinear principal component analysis (NLPCA) as a nonlinear generalisation of standard
principal component analysis (PCA) means to generalise the principal components from …

Motivation: Visualizing and analysing the potential non-linear structure of a dataset is
becoming an important task in molecular biology. This is even more challenging when the …

We propose kTDSEP, a kernel-based algorithm for nonlinear blind source separation (BSS).
It combines complementary research fields: kernel feature spaces and BSS using temporal …

Deep generative models, such as deep Boltzmann machines, focused on models that
provided parametric specification of probability distribution functions. Such models are …

An expectation-maximization algorithm for learning sparse and overcomplete data
representations is presented. The proposed algorithm exploits a variational approximation to …