19 citations · 27 across the 6 of their papers we have counts for
7 papers
Shape irregularity of Life-Like Network Automaton rules as an indicator of classification performance
Lucas C. S. Oliveira, Michiel Rollier, Jan Baetens +1
Complex Network (CN) classification requires high-level structural characterizations that are both scale-invariant and computationally efficient. Methods based on Life-Like Network…
Exact Lyapunov spectra of affine cellular automata and the parity rule on networks
Michiel Rollier, Jan M. Baetens
The Lyapunov exponent quantifies the sensitivity of a dynamical system to perturbations, and the full Lyapunov spectrum extends this to every orthogonal direction in tangent space.…
Essential metrics for Life on graphs
Michiel Rollier, Lucas Caldeira de Oliveira, Odemir M. Bruno +1
We present a strong theoretical foundation that frames a well-defined family of outer-totalistic network automaton models as a topological generalisation of binary outer-totalistic…
Convolutional Neural Networks for Automated Cellular Automaton Classification
Michiel Rollier, Aisling J. Daly, Jan M. Baetens
The emergent dynamics in spacetime diagrams of cellular automata (CAs) is often organised by means of a number of behavioural classes. Whilst classification of elementary CAs is fe…
Efficient Simulation of Non-uniform Cellular Automata with a Convolutional Neural Network
Michiel Rollier, Aisling J. Daly, Odemir M. Bruno +1
Cellular automata (CAs) and convolutional neural networks (CNNs) are closely related due to the local nature of information processing. The connection between these topics is benef…
A Comprehensive Taxonomy of Cellular Automata
Michiel Rollier, Kallil M. C. Zielinski, Aisling J. Daly +2
Cellular automata (CAs) are fully-discrete dynamical models that have received much attention due to the fact that their relatively simple setup can nonetheless express highly comp…
Mobility and the spatial spread of SARS-CoV-2 in Belgium
Michiel Rollier, Gisele H. B. Miranda, Jenna Vergeynst +4
We analyse and mutually compare time series of COVID-19-related data and mobility data across Belgium's 43 arrondissements (NUTS 3). In this way, we reach three conclusions. First,…