scholarly journals Combinatorial interpretation of Kolmogorov complexity

2002 ◽  
Author(s):  
A. Romashchenko ◽  
A. Shen ◽  
N. Vereshchagin
Keyword(s):  
Kolmogorov Complexity ◽  
Combinatorial Interpretation

scholarly journals Combinatorial interpretation of Kolmogorov complexity

2002 ◽  
Vol 271 (1-2) ◽  
pp. 111-123 ◽  
Author(s):  
A. Romashchenko ◽  
A. Shen ◽  
N. Vereshchagin
Keyword(s):  
Kolmogorov Complexity ◽  
Combinatorial Interpretation

Computable Følner monotilings and a theorem of Brudno

2020 ◽  
pp. 1-28
Author(s):  
NIKITA MORIAKOV
Keyword(s):  
Kolmogorov Complexity

Abstract A theorem of Brudno says that the Kolmogorov–Sinai entropy of an ergodic subshift over $\mathbb {N}$ equals the asymptotic Kolmogorov complexity of almost every word in the subshift. The purpose of this paper is to extend this result to subshifts over computable groups that admit computable regular symmetric Følner monotilings, which we introduce in this work. For every $d \in \mathbb {N}$ , the groups $\mathbb {Z}^d$ and $\mathsf{UT}_{d+1}(\mathbb {Z})$ admit computable regular symmetric Følner monotilings for which the required computing algorithms are provided.

scholarly journals Jacobi Ensemble, Hurwitz Numbers and Wilson Polynomials

2021 ◽  
Vol 111 (3) ◽  
Author(s):  
Massimo Gisonni ◽  
Tamara Grava ◽  
Giulio Ruzza
Keyword(s):  
Generating Functions ◽  
Combinatorial Interpretation ◽  
Hurwitz Numbers ◽  
Jacobi Ensemble ◽  
Matrix Ensembles ◽  
Wilson Polynomials ◽  
Unitary Ensemble ◽  
Topological Expansion

AbstractWe express the topological expansion of the Jacobi Unitary Ensemble in terms of triple monotone Hurwitz numbers. This completes the combinatorial interpretation of the topological expansion of the classical unitary invariant matrix ensembles. We also provide effective formulæ for generating functions of multipoint correlators of the Jacobi Unitary Ensemble in terms of Wilson polynomials, generalizing the known relations between one point correlators and Wilson polynomials.

scholarly journals Π10 classes and strong degree spectra of relations

2007 ◽  
Vol 72 (3) ◽  
pp. 1003-1018 ◽  
Author(s):  
John Chisholm ◽  
Jennifer Chubb ◽  
Valentina S. Harizanov ◽  
Denis R. Hirschfeldt ◽  
Carl G. Jockusch ◽  
...  
Keyword(s):  
Kolmogorov Complexity ◽  
Initial Segment ◽  
Truth Table ◽  
Linear Ordering ◽  
Computable Structures ◽  
Degree Spectra

AbstractWe study the weak truth-table and truth-table degrees of the images of subsets of computable structures under isomorphisms between computable structures. In particular, we show that there is a low c.e. set that is not weak truth-table reducible to any initial segment of any scattered computable linear ordering. Countable subsets of 2ω and Kolmogorov complexity play a major role in the proof.

scholarly journals The information complexity of learning tasks, their structure and their distance

2021 ◽  
Author(s):  
Alessandro Achille ◽  
Giovanni Paolini ◽  
Glen Mbeng ◽  
Stefano Soatto
Keyword(s):  
Kolmogorov Complexity ◽  
Large Scale ◽  
Parametric Model ◽  
Training Dataset ◽  
Optimization Scheme ◽  
Learning Tasks ◽  
Asymmetric Distance ◽  
Special Cases ◽  
Scale Models ◽  
Real World Datasets

Abstract We introduce an asymmetric distance in the space of learning tasks and a framework to compute their complexity. These concepts are foundational for the practice of transfer learning, whereby a parametric model is pre-trained for a task, and then fine tuned for another. The framework we develop is non-asymptotic, captures the finite nature of the training dataset and allows distinguishing learning from memorization. It encompasses, as special cases, classical notions from Kolmogorov complexity and Shannon and Fisher information. However, unlike some of those frameworks, it can be applied to large-scale models and real-world datasets. Our framework is the first to measure complexity in a way that accounts for the effect of the optimization scheme, which is critical in deep learning.

Sign in / Sign up

Export Citation Format

Share Document