The data of interest are assumed to be represented as N-dimensional real vectors, and
these vectors are compressible in some linear basis B, implying that the signal can be …

Time series data provided by single-molecule Förster resonance energy transfer (smFRET)
experiments offer the opportunity to infer not only model parameters describing molecular …

We develop a family of Bayesian algorithms built around Gaussian processes for various
problems posed by sensor networks. We firstly introduce an iterative Gaussian process for …

In this paper, we propose a new research problem on active learning from data streams,
where data volumes grow continuously, and labeling all data is considered expensive and …

We propose a unified global entropy reduction maximization (GERM) framework for active
learning and semi-supervised learning for speech recognition. Active learning aims to select …

A Bayesian dynamic model based on multitask learning (MTL) is developed for radar
automatic target recognition (RATR) using high-resolution range profile (HRRP). The aspect …

The use of the variational Bayes (VB) approximation in Bayesian filtering is studied, both as
a means to accelerate marginalized particle filtering and as a deterministic local (one-step) …

Little is currently known about dynamic brain networks involved in high-level cognition and
their ontological basis. Here we develop a novel Variational Bayesian Hidden Markov Model …

There are many sensing challenges for which one must balance the effectiveness of a given
measurement with the associated sensing cost. For example, when performing a diagnosis …

Feature selection is the process of reducing the number of collected features to a relevant
subset of features and is often used to combat the curse of dimensionality. This paper …