Nowadays, a large amount of information has to be transmitted or processed. This implies
high-power processing, large memory density, and increased energy consumption. In …

Sparse signal approximations have become a fundamental tool in signal processing with
wide-ranging applications from source separation to signal acquisition. The ever-growing …

A central goal in theoretical neuroscience is to predict the response properties of sensory
neurons from first principles. To this end,“efficient coding” posits that sensory neurons …

We present a new way of extending independent components analysis (ICA) to
overcomplete representations. In contrast to the causal generative extensions of ICA which …

This book brings together a collection of articles on statistical methods relating to missing
data analysis, including multiple imputation, propensity scores, instrumental variables, and …

We present a Bayesian approach for blind separation of linear instantaneous mixtures of
sources having a sparse representation in a given basis. The distributions of the coefficients …

Causal discovery with latent confounders is an important but challenging task in many
scientific areas. Despite the success of some overcomplete independent component …

Redundancy reduction has been proposed as the main computational process in the
primary sensory pathways in the mammalian brain. This idea has led to the development of …

In this paper, we present a graphical model for polyphonic music transcription. Our model,
formulated as a dynamical Bayesian network, embodies a transparent and computationally …

In this paper sparseness measures are reviewed, extended and compared. Special attention
is paid on measuring sparseness of noisy data. We review and extend several definitions …