Chapitre France de la Computational Intelligence Society IEEE

Accueil > À propos


Co-organisé par :
the IEEE Computational Intelligence Society,
the IEEE France Section CIS Chapter
and LIP6
le 27 Mars 2023 de 9h30 à 17h00
Lieu: LIP6, Sorbonne Université
4 place Jussieu, 75005 Paris
Tour 26, 1er étage, couloir 25-26, salle 105 "Jacques Pitrat".

Participation gratuite sur inscription à l'adresse suivante :

Les informations de connexion pour une participation virtuelle seront fournies aux participants inscrits.

Programme détaillé disponible sur

Aperçu du programme :

9:30-9:45James Keller
University of Missouri-Columbia, USA
President of the IEEE CIS
Introduction to the workshop
9:45-10:15Yaochu Jin
Faculty of Technology, Bielefeld University, Germany
From Federated Learning to Federated Data-Driven Optimization
10:15-10:45Marley Vellasco
Pontifical Catholic University of Rio de Janeiro, Brazil
Towards Automated Machine Learning (AutoML): Neuro-evolutionary models based on quantum-inspired evolutionary algorithm
10:45-11:00Coffee break
11:00-11:30James Keller
University of Missouri-Columbia, USA
Streaming Data Analytics: Clustering or Classification?
11:30-12:00Pablo Estevez
University of Chile - Millennium Institute of Astrophysics, Chile
Deep Attention-Based Models Transformers): Applications to Astronomy and Medicine
12:00-12:30Pau-Choo (Julia) Chung
National Cheng Kung University, Taiwan
ALOVAS: Digital Pathology image analyzer
12:30-14:30Lunch break
14:30-15:00Marcin Detyniecki
AXA, France
Making AI responsible : can we really?
Introduction to Quantum-Inspired Evolutionary Algorithms and their Application to Neural Architecture Search
15:00-15:30Marie-Jeanne Lesot
Sorbonne Université, France
Artificial Intelligence:
Explainable ?<=>? Trustworthy
15:30-16:00Piero P. Bonissone
Piero P Bonissone Analytics, LLC, CEO
Prognostics and Health Management (PHM) for Industrial AI: Leveraging Model Ensembles
16:00-17:00Drinks and networking


Developmental Machine Learning, Curiosity and Deep Reinforcement Learning

Le IEEE France Section's Life Members Affinity Group et le chapitre français de l'IEEE Computational Intelligence Society sont heureux de vous convier au séminaire de Pierre-Yves Oudeyer, Directeur de recherche à l’Inria et responsable de l’équipe FLOWERS à l’Inria et l’Ensta ParisTech.
Date et heure: 2 mars à 17:00

Résumé : Current approaches to AI and machine learning are still fundamentally limited in comparison with autonomous learning capabilities of children. What is remarkable is not that some children become world champions in certains games or specialties: it is rather their autonomy, flexibility and efficiency at learning many everyday skills under strongly limited resources of time, computation and energy. And they do not need the intervention of an engineer for each new task (e.g. they do not need someone to provide a new task specific reward function).
I will present a research program (Kaplan and Oudeyer, 2004; Oudeyer et al., 2007; Gottlieb and Oudeyer, 2019) that has focused on computational modeling of child development and learning mechanisms in the last decade. I will discuss several developmental forces that guide exploration in large real world spaces, starting from the perspective of how algorithmic models can help us understand better how they work in humans, and in return how this opens new approaches to autonomous machine learning.
In particular, I will discuss models of curiosity-driven autonomous learning, enabling machines to sample and explore their own goals and their own learning strategies, self-organizing a learning curriculum without any external reward or supervision. I will introduce the Intrinsically Motivated Goal Exploration Processes (IMGEPs-) algorithmic framework, and present two families of IMGEPs: population-based IMGEPs (Baranes and Oudeyer, 2013; Forestie et al.,2017) with learned goal spaces (Pere et al., 2018), which have allowed sample efficient learning learning of skill repertoires in real robots, and goal-conditioned Deep RL-based IMGEPs, which enable strong generalization properties when they are modular (Colas et al., 2019), in particular when leveraging the compositionality of language to imagine goals in curiosity-driven exploration (Colas et al., 2020).

Bio : Dr. Pierre-Yves Oudeyer is Research Director (DR1) at Inria and head of the Inria and Ensta-ParisTech FLOWERS team (France). Before, he has been a permanent researcher in Sony Computer Science Laboratory for 8 years (1999-2007). He studied theoretical computer science at Ecole Normale Supérieure in Lyon, and received his Ph.D. degree in artificial intelligence from the University Paris VI, France. He has been studying lifelong autonomous learning, and the self-organization of behavioural, cognitive and cultural structures, at the frontiers of artificial intelligence, machine learning, cognitive sciences and educational technologies. He has been developing models of intrinsically motivated learning, pioneering curiosity-driven learning algorithms working in real world robots, and developed theoretical frameworks to understand better human curiosity and autonomous learning. He also studied mechanisms enabling machines and humans to discover, invent, learn and evolve communication systems. He has published two books, more than 100 papers in international journals and conferences, holds 8 patents, gave several invited keynote lectures in international conferences, and received several prizes for his work in developmental robotics and on the origins of language. In particular, he is laureate of the Inria-National Academy of Science young researcher prize in computer sciences, and of an ERC Starting Grant EXPLORERS. He is also editor of IEEE CIS Newsletter on Cognitive and Developmental Systems where he organizes interdisciplinary dialogs in cognitive science, AI and robotics, as well as associate editor of IEEE Transactions on Cognitive and Developmental Systems and Frontiers in Neurorobotics. He has been chair of IEEE CIS Technical Committee on Cognitive and Developmental Systems.


Deep Learning Networks for Medical Image Analysis: its past, future, and issues

Le chapitre Computational Intelligence de l’IEEE France Section a le plaisir de vous inviter au séminaire du Prof. Pau-Choo (Julia) Chung, patronnée par l’IEEE Computational Intelligence Society dans son Distinguished Lecturer Program et l’IEEE France Section.
Date et horaire : 19 novembre à 9h

Résumé : Recent advancement of image understanding with deep learning neural networks has brought great attraction to those in image analysis into the focus of deep learning networks. While researchers on video/image analysis have jumped on the bandwagon of deep learning networks, medical image analyzers would be the coming followers. The characteristics of medical images are extremely different from those of photos and video images. The application of medical image analysis is also much more critical. For achieving the best effectiveness and feasibility of medical image analysis with deep learning approaches, several issues have to be considered. In this talk we will give a brief overview of the development of neural networks for medical image analysis in the past and the future trends with deep learning. Several issues in regard of the data preparation, techniques, and clinic applications will also be discussed.

Biographie : Pau-Choo (Julia) Chung (S'89-M'91-SM'02-F'08) received the Ph.D. degree in electrical engineering from Texas Tech University, USA, in 1991. She then joined the Department of Electrical Engineering, National Cheng Kung University (NCKU), Taiwan, in 1991 and has become a full professor in 1996. She served as the Head of Department of Electrical Engineering (2011-2014), the Director of Institute of Computer and Communication Engineering (2008-2011), the Vice Dean of College of Electrical Engineering and Computer Science (2011), the Director of the Center for Research of E-life Digital Technology (2005-2008), and the Director of Electrical Laboratory (2005-2008), NCKU. She was elected Distinguished Professor of NCKU in 2005 and received the Distinguished Professor Award of Chinese Institute of Electrical Engineering in 2012. She also served as Program Director of Intelligent Computing Division, Ministry of Science and Technology (2012-2014), Taiwan. She was the Director General of the Department of Information and Technology Education, Ministry of Education (2016-2018). She served the Vice President for Members Activities, IEEE CIS (2015-2018).

Dr. Chung's research interests include computational intelligence, medical image analysis, video analysis, and pattern recognition. Dr. Chung participated in many international conferences and society activities. She served as the program committee member in many international conferences. She served as the Publicity Co-Chair of WCCI 2014, SSCI 2013, SSCI 2011, and WCCI 2010. She served as an Associate Editor of IEEE Transactions on Neural Network and Learning Systems(2013-2015) and the Associate Editor of IEEE Transactions on Biomedical Circuits and Systems.

Dr. Chung was the Chair of IEEE Computational Intelligence Society (CIS) (2004-2005) in Tainan Chapter, the Chair of the IEEE Life Science Systems and Applications Technical Committee (2008-2009). She was a member in BoG of CAS Society (2007-2009, 2010-2012). She served as an IEEE CAS Society Distinguished Lecturer (2005-2007) and the Chair of CIS Distinguished Lecturer Program (2012-2013). She served on two terms of ADCOM member of IEEE CIS (2009-2011, 2012-2014), the Chair of IEEE CIS Women in CI (2014). She is a Member of Phi Tau Phi honor society and is an IEEE Fellow since 2008.


Le chapitre Computational Intelligence de l’IEEE France Section a le plaisir de vous inviter à deux événements organisés à l'occasion de la visite du Prof. Dr. Rudolf Kruse, patronnée par l’IEEE Computational Intelligence Society dans son Distinguished Lecturer Program et l’IEEE France Section :

1. Une conférence intitulée « Decomposable Models: On Learning, Fusion and Revision » le 7 mars 2019 à 10h30

La conférence est ouverte à tous. Voir le résumé et la biographie ci-dessous.

2. Une masterclass intitulée « Intelligent systems » avec le Prof. Dr. Rudolf Kruse, le 8 mars 2019 à 10h00

L'événement est ouvert à tous. Les doctorants intéressés pour présenter leurs travaux de thèse en 15 minutes, avant une discussion avec lui, sont priés de se faire connaître auprès d'Adrien Revault d'Allonnes ( avant le 7 mars 2019, midi.

Lieu des deux événements : LIP6, salle 105 (1er étage), couloir 25-26, 4 place Jussieu, 75005 Paris.

Abstract: Decomposable Graphical Models are of high relevance for complex industrial applications. The Markov network approach is one of their most prominent representatives and an important tool to decompose uncertain knowledge in high dimensional domains. But also relational and possibilistic decompositions turn out to be useful to make reasoning in such domains feasible. Compared to conditioning a decomposable model on given evidence, the learning of the structure of the model from data as well as the fusion of several decomposable models is much more complicated. The important belief change operation revision has been almost entirely disregarded in the past, although the problem of inconsistencies is of utmost relevance for real world applications. In this talk these problems are addressed by presenting several successful complex industrial applications.
Short bio: Rudolf Kruse is Professor at the Faculty of Computer Science at University of Magdeburg in Germany. He obtained his Ph.D. and his Habilitation in Mathematics from the Technical University of Braunschweig in 1980 and 1984 respectively. Following a stay at the Fraunhofer Gesellschaft, he joined the Technical University of Braunschweig as a professor of computer science in 1986. Since 1996 he is a professor in the Computational Intelligence Group in Magdeburg. He has coauthored 15 monographs and 25 books as well as more than 350 peer-refereed scientific publications in various areas with 16000 citations. He is associate editor of several scientific journals. Rudolf Kruse is Fellow of the International Fuzzy Systems Association (IFSA), Fellow of the European Association for Artificial Intelligence (EURAI/ECCAI ), and Fellow of the Institute of Electrical and Electronics Engineers (IEEE). His group is successful in various industrial applications in cooperation with companies such as Volkswagen, SAP, Daimler, and British Telecom. His current main research interests include data science and intelligent systems.


Le chapitre Computational Intelligence de l’IEEE France Section et le département DAPA du LIP6 ont le plaisir de vous inviter à la conférence intitulée
« Big Data Analytics using Deep Learning and Information Theoretical Learning: Applications to Astronomy »
qui sera donnée par le Professeur Pablo A. Estévez, Department of Electrical Engineering, University of Chile, and Millennium Institute of Astrophysics, Chile, Président de l’IEEE Computational Intelligence Society (2016-2017).
Date et horaire : 4 juillet 2017 à 10h
Lieu : LIP6, salle 105 (1er étage), couloir 25-26
4 place Jussieu, 75005 Paris.
Abstract: Astronomy is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new sky surveys. To cope with a change of paradigm to data-driven science new computational intelligence, machine learning and statistical approaches are needed. In this talk I will present two main applications. The first is to discriminate periodic versus non-periodic light curves, and then estimate the period of the periodic ones. Light curves are one-dimensional time series of the brightness of a star versus time. We have developed several methods based on the correntropy function (generalized correlation using information theoretical learning concepts), which outperforms conventional approaches. Results using 32.8 million light curves will be presented. Interestingly, some of these techniques can be applied to other problems such as sleep EEG analysis, and I will present preliminary results on this topic too.
The second application is the automated real-time transient detection in astronomical images. The aim is to achieve real-time detection of supernovae and other transients with the Dark Energy Camera. A novel transient detection pipeline was developed. We have been applying convolutional neural nets (deep learning) to discriminate between true transients and bogus transients, among other techniques, e.g non-negative matrix factorization combined with random forests. Results using 1.5 million images will be presented. The new pipeline was successfully tested online in February 2015 finding more than 100 supernovae in a few days of telescope observation.
Short bio: Pablo A. Estévez received his professional title in electrical engineering (EE) from Universidad de Chile, in 1981, and the M.Sc. and Dr.Eng. degrees from the University of Tokyo, Japan, in 1992 and 1995, respectively. He is a Full Professor with the Electrical Engineering Department, University of Chile, and former Chairman of the EE Department in the period 2006-2010.
Prof. Estévez is one of the founders of the Millennium Institute of Astrophysics (MAS), Chile, which was created in January 2014. He is currently leading the Astroinformatics/Astrostatistics group at MAS. He has been an Invited Researcher with the NTT Communication Science Laboratory, Kyoto, Japan; the Ecole Normale Supérieure, Lyon, France, and a Visiting Professor with the University of Tokyo.
Prof. Estévez is an IEEE Fellow. He is currently the President of the IEEE Computational Intelligence Society (CIS) for the term 2016-2017. He has served as IEEE CIS President-elect (2015), CIS Vice-president of Members Activities (2011-2014), CIS ADCOM Member-at-Large (2008-2010), CIS Distinguished Lecturer (2006-2011) and as an Associate Editor of the IEEE Transactions on Neural Networks (2007-2012).
Prof. Estévez served as conference chair of the International Joint Conference on Neural Networks (IJCNN), held in July 2016, in Vancouver, Canada, and general chair of the Workshop on Self-Organizing Maps (WSOM), held in December 2012, in Santiago, Chile. Currently he is serving as general co-chair of the 2018 IEEE World Congress on Computational Intelligence, WCCI 2018, to be held in Rio de Janeiro, Brazil, July 2018.
His current research interests include big data, deep learning, neural networks, self-organizing maps, data visualization, feature selection, information theoretic-learning, time series analysis, and advanced signal and image processing. One of his main topics of research is the application of computational intelligence techniques to astronomical datasets, and EEG signals.
Page web: link_to (


Séminaire du Professeur Ronald R. Yager
Distinguished lecturer de l’IEEE Computational Intelligence Society

Multi-Criteria Decision Making and Uncertainty
L’IEEE France Section et l’IEEE Computational Intelligence Society ont le plaisir de vous inviter à la présentation du professeur Ronald R. Yager, directeur du Machine Intelligence Institute, Iona College, USA, reçu en tant que distinguished lecturer. La présentation portera sur la décision multicritère dans l'incertain, son résumé est disponible ci-dessous.
Date et horaire : 4 mai 2017 à 10H00
Lieu : LIP6, salle 105 (1er étage), couloir 25-26
4 place Jussieu, 75005 Paris.

Abstract: Multi-Criteria aggregation is a pervasive problem appearing in many technological domains. During this presentation, we shall discuss some issues related to this task. One issue is the modeling of multi-criteria decision functions and a related issue is the evaluation of these decision functions in the face of uncertain information. One case we shall consider is the evaluation of the OWA operator when the satisfaction to the individual criteria is expressed via a probability distribution. We shall also consider the case of interval criteria satisfactions. We shall look at the role of fuzzy measures in the modeling process. One issue that must be dealt with is the ordering of the complex uncertain criteria satisfactions that is required to use the Choquet integral in the criteria aggregation.
Bio: Ronald R. Yager is Director of the Machine Intelligence Institute and Professor of Information Systems at Iona College. He is editor-in-chief of the International Journal of Intelligent Systems. He has published over 500 papers and edited over 30 books in areas related to fuzzy sets, human behavioral modeling, decision-making under uncertainty and the fusion of information. He is among the world’s most highly cited researchers with over 57,000 citations in Google Scholar. He was the 2016 recipient of the IEEE Frank Rosenblatt Award, the most prestigious honor given out by the IEEE Computational Intelligence Society. He was the recipient of the IEEE Computational Intelligence Society Pioneer award in Fuzzy Systems. He received the special honorary medal of the 50-th Anniversary of the Polish Academy of Sciences. He received the Lifetime Outstanding Achievement Award from the International Fuzzy Systems Association. He received honorary doctorate degrees, honoris causa, from the Azerbaijan Technical University and the State University of Information Technologies, Sofia Bulgaria. Dr. Yager is a fellow of the IEEE, the New York Academy of Sciences and the Fuzzy Systems Association. He has served at the National Science Foundation as program director in the Information Sciences program. He was a NASA/Stanford visiting fellow and a research associate at the University of California, Berkeley. He has been a lecturer at NATO Advanced Study Institutes. He was a visiting distinguished scientist at King Saud University, Riyadh Saudi Arabia. He was an honorary professor at Aalborg University in Denmark. He received his undergraduate degree from the City College of New York and his Ph. D. from the Polytechnic Institute New York University. He recently edited a volume entitled Intelligent Methods for Cyber Warfare.


Je suis heureuse de vous annoncer que nous recevrons le président de la IEEE Computational Intelligence Society le jeudi 22 octobre à 10h au LIP6, pour le séminaire suivant:

Ensemble Approaches in Learning
by Xin Yao, Professor at the University of Birmingham (UK), President of the IEEE Computational Intelligence Society

Lieu : salle 105, couloir 25-26, 4 place Jussieu, 75005 Paris

Résumé :
Designing a monolithic system for a large and complex learning task is hard. Divide-and-conquer is a common strategy in tackling such large and complex problems. Ensembles can be regarded an automatic approach towards automatic divide-and-conquer. Many ensemble methods, including boosting, bagging,
negative correlation, etc., have been used in machine learning and data mining for many years. This talk will describe three examples of ensemble methods,
i.e., multi-objective learning, online learning with concept drift, and multi-class imbalance learning. Given the important role of diversity in ensemble methods, some discussions and analyses will be given to gain a better understanding of how and when diversity may help ensemble learning.

Some materials used in the talk are based on the following papers:

A Chandra and X. Yao, ``Ensemble learning using multi-objective evolutionary
algorithms,'' Journal of Mathematical Modelling and Algorithms, 5(4):417-445,
December 2006.

L. L. Minku and X. Yao, "DDD: A New Ensemble Approach For Dealing With Concept
Drift,'' IEEE Transactions on Knowledge and Data Engineering, 24(4):619-633,
April 2012.

S. Wang and X. Yao, ``Multi-Class Imbalance Problems: Analysis and Potential
Solutions,'' IEEE Transactions on Systems, Man and Cybernetics, Part B,
42(4):1119-1130, August 2012.

Bio :
Xin Yao is a Chair (Professor) of Computer Science and the Director of CERCIA (Centre of Excellence for Research in Computational Intelligence and
Applications) at the University of Birmingham, UK. He is an IEEE Fellow and the President (2014-15) of IEEE Computational Intelligence Society (CIS). His
work won the 2001 IEEE Donald G. Fink Prize Paper Award, 2010 and 2015 IEEE Transactions on Evolutionary Computation Outstanding Paper Awards, 2010 BT Gordon Radley Award for Best Author of Innovation (Finalist), 2011 IEEE Transactions on Neural Networks Outstanding Paper Award, and many other best
paper awards. He won the prestigious Royal Society Wolfson Research Merit Award in 2012 and the 2013 IEEE CIS Evolutionary Computation Pioneer Award.
His major research interests include evolutionary computation, ensemble learning, and their applications, especially in software engineering.

Plus d'information sur Xin Yao :

Bernadette Bouchon-Meunier
Présidente, IEEE France Section Computational Intelligence Chapter


Nous sommes heureux d'annoncer le séminaire de Prof. Jim Keller, de l'University of Missouri, aux Étas-Unis, le 28 mai 2015, qui nous parlera de "Does it all add up? A study of fuzzy protoform linguistic summarization of time series", invité par la Computational Intelligence Society, dans le cadre de son Distinguished Lecturer Program. Le résumé de sa présentation est :

Producing linguistic summaries of large databases or temporal sequences of measurements is an endeavor that is receiving increased attention. These summaries can be used in a continuous monitoring situation, like eldercare, where it is important to ascertain if the current summaries represent an abnormal condition. Primarily a human, such as a care giver in the eldercare
example, is the recipient of the set of summaries describing a time range, for example, last night’s activities. However, as the number of sensors and monitored conditions grow, sorting through a fairly large number of summaries can be a burden for the person, i.e., the summaries stop being information and become yet one more pile of data. It is therefore necessary to automatically process sets of summaries to condense the data into more manageable chunks.
The first step towards automatically comparing sets of digests is to determine similarity. For fuzzy protoform based summaries, we developed a natural similarity and proved that the associated dissimilarity is a metric over the space of protoforms. Utilizing that distance measure, we defined and examined several fuzzy set methods to compute dissimilarity between sets of summaries, and most recently utilized these measures to define prototypical behavior over a large number of normal time periods.
In this talk, I will cover the definition of fuzzy protoforms, define our (dis)similarity, outline the proof that it is a metric, discuss the fuzzy aggregation methods for sets of summaries, and show how prototypes are formed and can used to detect abnormal nights. The talk will be loaded with actual examples from our eldercare research. There is much work to be done and hopefully, more questions than answers will result from the discussion.

La liste complète de nos séminaires est disponible ici et les prochains sont \
consultables .