We consider the problem of identifying universal low-dimensional features from high-
dimensional data for inference tasks in settings involving learning. For such problems, we …

With the unprecedented performance achieved by deep learning, it is commonly believed
that deep neural networks (DNNs) attempt to extract informative features for learning tasks …

Contraction coefficients are distribution dependent constants that are used to sharpen
standard data processing inequalities for f-divergences (or relative f-entropies) and produce …

Quantum algorithms for data analysis Page 1 Quantum algorithms for data analysis
Alessandro Luongo 2024-12-08 Page 2 2 Page 3 Contents 1 Preface 7 1.1 Abstract …

This paper narrows the gap between previous literature on quantum linear algebra and
practical data analysis on a quantum computer, formalizing quantum procedures that speed …

A modal decomposition is a useful tool that deconstructs the statistical dependence between
two random variables by decomposing their joint distribution into orthogonal modes …

Correspondence analysis (CA) is a multivariate statistical tool used to visualize and interpret
data dependencies by finding maximally correlated embeddings of pairs of random …

Computing eigenvalue decomposition (EVD) of a given linear operator, or finding its leading
eigenvalues and eigenfunctions, is a fundamental task in many machine learning and …

For the past decades, information theory and machine learning have propelled each other
forward. Information theory has provided mathematical tools to tackle emerging challenges …

Over the past decade, artificial neural networks have ushered in a revolution in science and
society. Nowadays, neural networks are applied to various problems such as speech …