Abstract: Over the past decades, there has been a surge of interest in studying low-dimensional structures within high-dimensional data. Statistical factor models (i.e., low-rank plus diagonal ...

TO THE EDITOR: Artificial intelligence (AI) systems, and computers in general, possess several advantages over humans. They have virtually perfect recall and are not subject to fatigue, mood ...

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...

Clustering is usually the first exploratory analysis step in empirical data. When the data set comprises graphs, the most common approaches focus on clustering its vertices. In this work, we are ...

Abstract: Message passing algorithms have had dramatic impacts on important problems in signal processing, learning theory, communication theory, and information theory through their computational ...

The expectation-maximization algorithm maximises the likelihood function for problems involving latent or hidden variables. Latent variables are unobservable random variables that can introduce ...

There’s pessimism. There’s optimism. Then there’s another, lesser explored but deeply significant state — expectation. That feeling of knowing, or believing we know, what’s going to happen. And while ...

China is once more tightening its grip on internet content, and this time algorithms are in the spotlight. The Cyberspace Administration of China has published upcoming rules that dictate how internet ...

We recently developed a family of image reconstruction algorithms that look like the emission maximum-likelihood expectation-maximization (ML-EM) algorithm. In this study, we extend these algorithms ...