[Event at CIG] MESS 2024 ~ autoDL meets HPC ~ 15-18 July 2024
Mario Pavone
mpavone at dmi.unict.it
Wed Feb 7 09:55:20 CET 2024
Call for Participation (apologies for multiple copies)
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MESS 2024 - Metaheuristics Summer School
- Automated Deep Learning meets High-Performance Computing -
15-18 July 2024, Catania, Italy
https://www.ANTs-lab.it/mess2024/
mess.school at ANTs-lab.it
https://www.facebook.com/groups/MetaheuristicsSchool/
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** APPLICATION DEADLINE: 16th March 2024 **
MESS 2024 is aimed at qualified and strongly motivated MSc and PhD
students; post-docs; young researchers, and both academic and
industrial professionals to provide them with an overview on the
metaheuristics techniques, and an in-depth analysis of the
state-of-the-art. It is a full-immersion four-days course in the
beautiful city of Catania whose goal is to offer all participants with
a stimulating environment to study and learn advanced concepts and
recent research in the fields of Metaheuristics, Optimization, Machine
Learning and Artificial Intelligence, in general.
The main theme of this edition is focused on ?Automated Deep Learning
Meets High-Performance Computing?, that is how these research areas
may interact and affect each other in order to develop reliable and
robust solving methodologies for Big Data analysis, and data-driven
problems.
Participants will be delivered a certificate of attendance indicating
the number of hours of lectures (36-40 hours of lectures). In
according to the academic system all PhD and master students attending
to the summer school will may get 8 ECTS points.
** LECTURERS
+ Thomas Bartz-Beielstein, TH Koln, Germany
Lecture#1: Introduction to Surrogate-Based Hyperparameter Tuning
in Classical Machine Learning, Online Machine Learning, and Deep
Learning
Lecture#2: Exploring Hyperparameter-Tuning Applications:
Optimizing scikit-learn, River, and PyTorch with SPOT and Other Tuning
Techniques
+ Aaron Klein, AWS Research Berlin, Germany
Lecture#1: Introduction into AutoML: Hyperparameter Optimization
Lecture#2: Introduction into AutoML: Neural Architecture Search
+ El-Ghazali Talbi, University of Lille1, France
Lecture#1: TBA
Lecture#2: TBA
~ More Lecturers will be announced soon ~
** METAHEURISTICS COMPETITION
All participants will be involved in the ?Metaheuristics Competition
Race?, where each of them, individually or divided in working groups
(no more than 3 people), will develop a metaheuristic solution for a
given problem presented during the school.
The top three of the competition ranking will receive the MESS 2024
award and will be involved in the writing of a manuscript dedicated to
the competition that, afterwards, will be submitted to an
international journal for possible publication. In addition, the
remaining best ten in the ranking (excluding the top three, of course)
will be invited to report their work in a manuscript which will be
published in the special MESS 2024 Volume of the AIRO Springer Series.
** SHORT ORAL PRESENTATION
All participants may submit an abstract of their recent results, or
works in progress, for presentation and having the opportunities for
debate and interact with leaders in the field. The Workshop Organizers
and Scientific Committee will review the abstracts and will recommend
for the format of the presentation (oral or poster). All abstracts
will be published on the electronic hands-out book of the summer school.
The Abstracts must be submitted during the application by *March 16, 2020*.
** MESS 2024 DIRECTORS:
- Salvatore Greco, University of Catania, Italy
- Mario Pavone, University of Catania, Italy
- Andrea Schaerf, University of Udine, Italy
- El-Ghazali Talbi, University of Lille1, France
- Daniele Vigo, University of Bologna, Italy
** METAHEURISTICS COMPETITION CHAIRS:
- Raffaele Cerulli, University of Salerno, Italy
- Sara Ceschia, University of Udine, Italy
- Mario Pavone, University of Catania, Italy
- Andrea Schaerf, University of Udine, Italy
** MORE INFORMATION:
https://www.ANTs-lab.it/mess2024/ -- mess.school at ANTs-lab.it
Facebook Group: https://www.facebook.com/groups/MetaheuristicsSchool/
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