Special Issue on "New Horizons at the Intersections of Machine Learning, Dynamical Systems, and Algorithmic Information Theory" in Physica D
Marcus Hutter
We
are pleased to announce an upcoming special issue in Physica D:
Nonlinear Phenomena, dedicated to exploring the cutting-edge
intersections of Machine Learning (ML), Dynamical Systems Theory
(DST), and Algorithmic Information Theory (AIT).
This
special issue aims to foster a rich, multidisciplinary dialogue
that uncovers new methodologies, theoretical insights, and
practical applications at the confluence of these pivotal areas.
Our goal is to push the boundaries of current knowledge and
provide innovative tools for the analysis and understanding of
complex systems.
Themes
and Objectives:
ML
for DST: Emphasizing data-driven methodologies to extend the
boundaries of the classical theory of dynamical systems.
DST
for ML: Applying DST principles to analyze ML algorithms viewed
as dynamical systems.
ML
for AIT: Employing ML to tackle core problems within AIT,
optimizing compression and prediction algorithms.
AIT
for ML: Exploring the theoretical underpinnings that predict ML
algorithmic performance and limitations.
DST
for AIT: Applying DST insights to algorithmic questions in AIT.
AIT
for DST: Leveraging algorithmic principles to deepen the
understanding of DST.
For
more details on this special issue and to view the call for
papers, visit: https://lnkd.in/d35vUCQx
Submit
your manuscripts at: https://lnkd.in/de6Cazqy.
Please select ‘VSI: MLDSAIT’ during submission.
We
invite researchers from across these disciplines to contribute
their insights and join us in this exciting exploration. Let’s
delve into the complex world of ML, DST, and AIT together!
MachineLearning DynamicalSystems AlgorithmicInformationTheory Research CallForPapers
Guest editors:
Dr. Boumediene Hamzi
Dr. Kamal Dingle
Prof. Marcus Hutter
Dr. Qinxiao Li
Dr. Tanya Schmah