Research on understanding and quantifying movement variability with nonlinear analyses has been well established in the last three decades in areas such as biomechanics, sport science, psychology, cognitive science, and neuroscience (davids et al. 2003). This work is hypothesising that nonlinear analyses can be used to quantify subtle variations of facial expressions that can be related to different mental states (i.e. pain, distrusful, relief, etc) (Back et al 2014). This hypothesis has then led the author to ask two research questions: how the quantification of facial expressions can be related to
the complexity of facial expressions?, and does the quantification of the complexity for facial expressions can tell us something about the state of mind of a person? Full abstract
If you use or adapat any of the files in this repository, use the following BibTeX code
@misc{miguel_xochicale_2019_2559629,
author = {Miguel Xochicale},
title = {{mxochicale/mlds2019: Release repository for the
symposium}},
month = feb,
year = 2019,
doi = {10.5281/zenodo.2559629},
url = {https://doi.org/10.5281/zenodo.2559629}
}
The intersection of the fields of dynamical systems and machine learning is largely unexplored, and the goal of this symposium is to bring together researchers from these fields to fill the gap between the theories of dynamical systems and machine learning in the following directions:
- Machine Learning for Dynamical Systems: how to analyze dynamical systems on the basis of observed data rather than attempt to study them analytically.
- Dynamical Systems for Machine Learning: how to analyze algorithms of Machine Learning using tools from the theory of dynamical systems.
Boumediene Hamzi, Yi-Ke Guo,
Jeroen Lamb, Diana O'Malley (Imperial College London) and
Robert MacKay (University of Warwick and The Alan Turing Institute)
If you have specific questions about the repository, you can contact Miguel Xochicale. If your question might be relevant to other people, please instead open an issue.