Search Results for author: Jesper Tegnér

Found 10 papers, 3 papers with code

Learning Functions in Large Networks requires Modularity and produces Multi-Agent Dynamics

2 code implementations9 Jul 2018 C. -H. Huck Yang, Rise Ooi, Tom Hiscock, Victor Eguiluz, Jesper Tegnér

We ask whether larger dynamical network motifs exist in biological networks, thus contributing to the higher-order organization of a network.

Algorithmic Causal Deconvolution of Intertwined Programs and Networks by Generative Mechanism

no code implementations18 Feb 2018 Hector Zenil, Narsis A. Kiani, Allan A. Zea, Jesper Tegnér

Complex data usually results from the interaction of objects produced by different generating mechanisms.

Minimal Algorithmic Information Loss Methods for Dimension Reduction, Feature Selection and Network Sparsification

2 code implementations16 Feb 2018 Hector Zenil, Narsis A. Kiani, Antonio Rueda-Toicen, Allan A. Zea, Jesper Tegnér

We introduce a family of unsupervised, domain-free, and (asymptotically) model-independent algorithms based on the principles of algorithmic probability and information theory designed to minimize the loss of algorithmic information, including a lossless-compression-based lossy compression algorithm.

Data Structures and Algorithms Information Theory Information Theory Physics and Society

An Algorithmic Information Calculus for Causal Discovery and Reprogramming Systems

no code implementations15 Sep 2017 Hector Zenil, Narsis A. Kiani, Francesco Marabita, Yue Deng, Szabolcs Elias, Angelika Schmidt, Gordon Ball, Jesper Tegnér

We demonstrate that the algorithmic information content of a system is deeply connected to its potential dynamics, thus affording an avenue for moving systems in the information-theoretic space and controlling them in the phase space.

Causal Discovery Dimensionality Reduction +1

Approximations of Algorithmic and Structural Complexity Validate Cognitive-behavioural Experimental Results

no code implementations21 Sep 2015 Hector Zenil, James A. R. Marshall, Jesper Tegnér

Being able to objectively characterise the intrinsic complexity of behavioural patterns resulting from human or animal decisions is fundamental for deconvolving cognition and designing autonomous artificial intelligence systems.

Causality, Information and Biological Computation: An algorithmic software approach to life, disease and the immune system

no code implementations24 Aug 2015 Hector Zenil, Angelika Schmidt, Jesper Tegnér

Here we further unpack ideas related to computability, algorithmic information theory and software engineering, in the context of the extent to which biology can be (re)programmed, and with how we may go about doing so in a more systematic way with all the tools and concepts offered by theoretical computer science in a translation exercise from computing to molecular biology and back.

The Information-theoretic and Algorithmic Approach to Human, Animal and Artificial Cognition

no code implementations17 Jan 2015 Nicolas Gauvrit, Hector Zenil, Jesper Tegnér

We survey concepts at the frontier of research connecting artificial, animal and human cognition to computation and information processing---from the Turing test to Searle's Chinese Room argument, from Integrated Information Theory to computational and algorithmic complexity.

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