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We introduce a uniform representation of general objects that captures the regularities with respect to their structure. It allows a representation of a general class of objects including geometric patterns and images in a sparse, modular,…

Computational Complexity · Computer Science 2008-06-12 Hiroshi Ishikawa

We survey diverse approaches to the notion of information: from Shannon entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov complexity are presented: randomness and classification. The survey is divided in two parts…

Logic in Computer Science · Computer Science 2010-10-20 Marie Ferbus-Zanda , Serge Grigorieff

Ergodic theory includes several notions of entropy for probability-preserving actions of countable groups. These include Kolmogorov--Sinai entropy based on F\o lner sequences for amenable groups, entropy defined using a random ordering of…

Operator Algebras · Mathematics 2026-03-23 Tim Austin

It is well known that normality can be described as incompressibility via finite automata. Still the statement and the proof of this result as given by Becher and Heiber (2013) in terms of "lossless finite-state compressors" do not follow…

Information Theory · Computer Science 2020-08-25 Alexander Kozachinskiy , Alexander Shen

Although information content is invariant up to an additive constant, the range of possible additive constants applicable to programming languages is so large that in practice it plays a major role in the actual evaluation of K(s), the…

Information Theory · Computer Science 2010-06-03 Jean-Paul Delahaye , Hector Zenil

We survey diverse approaches to the notion of information: from Shannon entropy to Kolmogorov complexity. Two of the main applications of Kolmogorov complexity are presented: randomness and classification. The survey is divided in two parts…

Logic in Computer Science · Computer Science 2010-10-15 Marie Ferbus-Zanda

We start with definitions of the general notions of the theory of $\Bbb Z_{2}$-graded algebras. Then we consider theory of inductive families of $\Bbb Z_{2}$-graded semisimple finite-dimensional algebras and its representations in the…

Representation Theory · Mathematics 2008-01-17 A. M. Vershik , A. N. Sergeev

We initiate the theory of communication complexity of individual inputs held by the agents, rather than worst-case or average-case. We consider total, partial, and partially correct protocols, one-way versus two-way, with and without help…

Computational Complexity · Computer Science 2007-05-23 Harry Buhrman , Hartmut Klauck , Nikolai Vereshchagin , Paul Vitanyi

In this paper, we revisit a central concept in Kolmogorov complexity in which one would equate program-size complexity with information content. Despite the fact that Kolmogorov complexity has been widely accepted as an objective measure of…

Information Theory · Computer Science 2017-10-19 Fouad B. Chedid

The Kolmogorov-Arnold representation is a proven adequate replacement of a continuous multivariate function by an hierarchical structure of multiple functions of one variable. The proven existence of such representation inspired many…

Optimization and Control · Mathematics 2020-06-23 Andrew Polar , Michael Poluektov

We investigate topological, combinatorial, statistical, and enumeration properties of finite graphs with high Kolmogorov complexity (almost all graphs) using the novel incompressibility method. Example results are: (i) the mean and variance…

Combinatorics · Mathematics 2007-05-23 Harry Buhrman , Ming Li , John Tromp , Paul Vitanyi

We survey the diverse approaches to the notion of information content: from Shannon entropy to Kolmogorov complexity. The main applications of Kolmogorov complexity are presented namely, the mathematical notion of randomness (which goes…

Logic · Mathematics 2008-01-03 Marie Ferbus-Zanda , Serge Grigorieff

The arithmetic of natural numbers has a natural and simple encoding within sets, and the simplest set whose structure is not that of any natural number extends this set-theoretic representation to positive and negative integers. The…

Logic · Mathematics 2019-05-17 Ruadhan O'Flanagan

We show that classical and quantum Kolmogorov complexity of binary strings agree up to an additive constant. Both complexities are defined as the minimal length of any (classical resp. quantum) computer program that outputs the…

Quantum Physics · Physics 2009-06-09 Markus Mueller

In analogy of classical Kolmogorov complexity we develop a theory of the algorithmic information in bits contained in any one of continuously many pure quantum states: quantum Kolmogorov complexity. Classical Kolmogorov complexity coincides…

Quantum Physics · Physics 2007-05-23 Paul Vitanyi

We introduce a machine free mathematical framework to get a natural formalization of some general notions of infinite computation in the context of Kolmogorov complexity. Namely, the classes Max^{X\to D}_{PR} and Max^{X\to D}_{Rec} of…

Logic · Mathematics 2008-01-07 Marie Ferbus-Zanda , Serge Grigorieff

We introduce algorithmic information theory, also known as the theory of Kolmogorov complexity. We explain the main concepts of this quantitative approach to defining `information'. We discuss the extent to which Kolmogorov's and Shannon's…

Information Theory · Computer Science 2008-09-17 Peter D. Grunwald , Paul M. B. Vitanyi

In the paper we present a description of complex systems in terms of self-organization processes of prime integer relations. A prime integer relation is an indivisible element made up of integers as the basic constituents following a single…

Adaptation and Self-Organizing Systems · Physics 2016-11-23 Victor Korotkikh , Galina Korotkikh

Mathematical notation makes up a large portion of STEM literature, yet finding semantic representations for formulae remains a challenging problem. Because mathematical notation is precise, and its meaning changes significantly with small…

Computation and Language · Computer Science 2023-09-06 Neeraj Gangwar , Nickvash Kani

One approach for interpreting black-box machine learning models is to find a global approximation of the model using simple interpretable functions, which is called a metamodel (a model of the model). Approximating the black-box with a…

Machine Learning · Computer Science 2023-02-10 Mahed Abroshan , Saumitra Mishra , Mohammad Mahdi Khalili