Liste de nos séminaires

(ordre anti-chronologique)

Séminaire DAPA du 19 / 12 / 2019 à 10h

Estimating music descriptions using convolutional neural networks

Alice Cohen (Sciences et technologies de la musique et du son, Sorbonne Université)

Lieu : LIP6, salle 405 (4ème étage), couloir 24-25, 4 place Jussieu, 75005 Paris

In Music Information Retrieval (MIR) and voice processing, the use of
machine learning tools has become in the last few years more and more
standard. Especially, many state-of-the-art systems now rely on the use
of Neural Network. More precisely, we will focus on convolutional
neural networks (ConvNet), a class of neural networks designed for image

To apply such networks to sound and music, several problems can arise.
We chose to study 3 of them:
- What is the impact of input representation? Which can of transform
can we use to inform the resolution of MIR problems? To answer those
questions, we will present works don on structure estimation and singing
voice separation.
- How can we gather large amount of labeled data ? We will present
two different strategies: one for singing voice detection using teacher
student paradigm and one for singing voice separation, using signal
processing tools for data augmentation.
- How can we use ConvNet not only to solve problems, but also to
validate solutions? To study this problem, we will present how we design
and evaluate a voice anonymization method in urban sound recordings.

Alice Cohen's bio

Alice Cohen was a PhD student at Institut de Recherche et de Coordination
Acoustique Musique (IRCAM). She defended her thesis, untitled "Estimating
music and sound descriptions using deep learning" last October. Her
research interests mostly focus on voice extraction and detection, with
signal processing and machine learning tools.

Plus d'information sur Alice Cohen :

Séminaire DAPA du 17 / 10 / 2019 à 11h

User Centric Data Exploration

Nicolas Labroche (Université François Rabelais de Tours)
Patrick Marcel (Université François Rabelais de Tours)

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

Traditional data exploration is an iterative process that generally starts with fuzzy initial user needs, and a prescriptive querying language such as SQL to extract meaningful information. Since their inception, most DBMSs have notoriously been optimized to ensure that massive queries could be answered very fast (see TPC benchmarks for example), ignoring new analytical needs, like for instance data enthusiasts' interactions to produce dashboards or insights from the data.

Our research group investigates user-centric approaches for data exploration, from the automatic elicitation of user intents, the evaluation of the exploration results based on what was interesting for the analyst and what she learned from interactions with the data, the proposal of new interactive clustering approaches, to the definition of a first user centered benchmark for exploration quality assessment.

This talk describes some of the approaches investigated, and is organized in three main parts. First, we present two approaches for modeling user profiles: (i) user intent elicitation and its application to bundling exploration queries for recommendation purpose, and (ii) subjective interestingness modeling and its application to estimate to which extent an interaction could be valuable for a user.
Next, we present a method to automatically assess the quality of explorations of multidimensional data.
Finally, our talk concludes with a description of an on-going work on the implementation of intentional exploration primitives.

Nicolas Labroche's bio

Nicolas Labroche research activity mainly focuses on user centered data mining approaches such as semi-supervised clustering, explainable machine learning or soft computing with an aim for interactive and interpretable methods with applications to personalization and recommendation. As such he has reviewed papers and participated in the committees of conferences and journals connected to these topics (Information Systems, Fuzz-IEEE, PKDD, PAKDD, DOLAP, EGC).

Plus d'information sur Nicolas Labroche :

Patrick Marcel's bio

Patrick Marcel is an active researcher in the field of OLAP and user-centered techniques (including personalization and recommendation). He has participated in the committees of conferences and journals connected to these topics (including DKE, VLDB, EDBT, DOLAP, DaWaK) and has organized EDA in 2013, eBISS in 2016 and DOLAP in 2017

Plus d'information sur Patrick Marcel :

Séminaire DAPA du 15 / 10 / 2019 à 10h

Optimizing Multiple Objectives for Clustering

Sanghamitra Bandyopadhyay (Indian Statistical Institute)

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

Clustering is an unsupervised exploratory data analysis tool which groups the
data on the basis of some similarity/dissimilarity metric such that a predefined
criterion is optimized. The problem of clustering is therefore essentially one
of optimization. The use of metaheuristics like genetic algorithms has been made
successfully in the past for clustering a data set. It is to be noted that the
clustering problem admits a number of criteria or cluster validity indices that
have to be simultaneously optimized for obtaining improved results. Hence in
recent times the problem has been posed in a multiobjective optimization (MOO)
framework and popular metaheuristics for multiobjective optimization have been
applied. In this talk, we will first briefly discuss about the fuzzy c-means
algorithm and the basic principles of MOO. Subsequently it will be shown how a
popular multiobjective optimization algorithm may be used for solving the
clustering problem. Since such algorithms provide a number of solutions, a way
of combining the multiple clustering solutions so obtained into a single one
using supervised learning will be explained. The talk will conclude by
demonstrating an application of the multiobjective clustering technique on gene
expression data sets.


Sanghamitra Bandyopadhyay joined the Machine Intelligence Unit of
the Indian Statistical Institute as a faculty member in 1999, after
completing her PhD from the same Institute.. She is currently the
Director of the Institute. Her areas of research interest include
computational biology and bioinformatics, soft and evolutionary
computation, pattern recognition and data mining. In these areas she has
published more than 300 research articles in various journal,
conferences and edited volumes. She has published six authored and
edited books from publishers like Springer, World Scientific and Wiley.
Sanghamitra has worked in various Universities and Institutes world-wide
including in USA, Australia, Germany, France, Italy, China, Slovenia and
Mexico, and delivered invited lectures in many more countries. She has
received several awards and fellowships including the Bhatnagar Prize,
Infosys award, TWAS Prize, DBT National Women Bioscientist Award
(Young), INAE Silver Jubilee Prize, Young scientist/engineer medals of
INSA, INAE and Science Congress, JC Bose Fellowship, Swarnajayanti
Fellowship and Humboldt Fellowship. She is a Senior Associate of ICTP
and Fellow of INSA, INAE, NASI and IEEE. She is currently a member of
the Science, Technology and Innovation Advisory Council of the Prime
Minister of India.

Plus d'information sur Sanghamitra Bandyopadhyay :

Séminaire DAPA du 8 / 10 / 2019 à 10h

Fuzziness in Action: Exploration and Exploitation with Linguistic Terms

Marek Reformat (University of Alberta)

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

Exploration and exploitation are two ways of conducting search processes.
Tools we use to interact with the web focus on providing us with possibly
the most accurate results. These tools try to find items as relevant as
possible to a set of entered search keywords. Yet, in order to truly
experience and take advantage of the vast amount of information gathered
on the web, we should be able to control a degree of precision in our
search process, i.e., we should be able to determine if we want to obtain
clear and precise results, or if we want a collection of broad and somehow
relevant findings. Furthermore, we would like to accomplish this in a
friendly, human-like way. So far, imprecision – or shall we say
diversification – of our search results is unplanned and happens
accidently. We do not have the means to control a degree or extensiveness
of that diversity while, at the same time, we initiate our queries
specifying a number of keywords.

In this presentation, we hypothesize that fuzzy-based methods are able to
address the above-mentioned issues. We say that application of linguistic
terms and their personal interpretations allows for adaptable and flexible
control of search processes. Imprecise formulation of search requirements
enables users to achieve a balance between exploration and exploitation.
Naturally, an intrinsic vagueness of the linguist terms makes the whole
process less accurate, but a larger collection of relevant things is
obtained leading to a true, browsing in-store experience. As a result,
users are able to experience and explore large range of findings that do
not match the search criteria to a high degree.

We will illustrate benefits of application for fuzzy methods via
presenting two scenarios: searching for users with different degrees of
similarity on a social network, and generating a list of recommendations
based on groups of users satisfying a set of criteria to different

Marek Reformat's bio

Marek Reformat received his MSc degree (with honors) from Technical
University of Poznan, Poland, and a PhD degree from University of
Manitoba, Canada. His initial research projects involved different
aspects related to computer networks, especially in the area of
management and performance measurement. He co-authored several papers
and reports regarding this topic. During his PhD studies, his research
interests included distributed computing, with emphasis on
fault-tolerant systems in such frameworks as Parallel Virtual Machine
(PVM) and Message Passing Interface (MPI); optimization methods; and
fuzzy sets and systems. His principle interest was related to
evolutionary computing and its application to optimization problems.
He proposed a new methodology for design of control systems, which
relied on a combination of advanced system simulators and genetic
computation. He applied this concept to the control design problem in
the area of power systems. In 1997 he joined the Manitoba HVDC
Research Centre, where he was a member of a simulation software
development team. He was involved in improvement and development of an
electromagnetic transients program for time-domain simulation,
performed functional and structural testing of the software, and
provided expert consulting services in the area of simulation and
modeling internationally. He has been with the Department of
Electrical and Computer Engineering at University of Alberta since
July 2000. He is Professor and Associate Chair of Graduate Studies in
the Department. In addition, he is an Associate Editor of a number of
journals related to computational intelligence and software
engineering. He has been a member of program committees of several
conferences related to those areas. He is actively involved in North
American Fuzzy Information Processing Society (NAFIPS). He is a member
of the IEEE and ACM. He is currently the president of the
International Fuzzy Systems Association (IFSA).

Marek stays at LIP6, in the LFI team, until October 14th, he benefits from the LIP6 Invited professor funding.

Plus d'information sur Marek Reformat :

Séminaire DAPA du 12 / 9 / 2019 à 10h

Apprentissage de représentations de documents liés

Adrien Guille (Université de Lyon, Laboratoire ERIC)

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

L'apprentissage de représentations de documents liés consiste à plonger un corpus structuré dans un espace vectoriel de faible dimension, en prenant en compte à la fois l'information textuelle et l'information structurelle.
Au cours de cet exposé, je présenterai des travaux récents à ce sujet, menés au sein de l'équipe DMD du laboratoire ERIC.
Premièrement, je présenterai une méthode pour apprendre conjointement des représentations orientées texte, et des représentations orientées structure (Brochier, Guille & Velcin, WWW 2019). J'illustrerai l'intérêt de telles représentations d'un point de vue qualitatif, à travers une tâche de suggestion de mots-clés.
Deuxièmement, je présenterai une nouvelle méthode, sans hyperparamètre et basée sur un algorithme d'estimation efficace, qui rend son utilisation particulièrement pratique (Guille & Gourru, soumis). Je démontrerai empiriquement, d'un point de vue quantitatif, la pertinence de cette méthode, tant en classification de documents qu'en prédiction de liens.


Adrien Guille est Maître de Conférences en informatique à l'Université Lumière Lyon 2 depuis 2016, membre de l'équipe Data Mining & Decision du laboratoire ERIC. Ses travaux de recherche émannent de problématiques concrètes liées à la production textuelle sur le Web. Après s'être intéressé durant son doctorat aux médias sociaux et à la manière dont ceux-ci réagissent aux évènements (hors ligne) et propagent l'information en leur sein, il s'intéresse désormais à l'apprentissage de représentations de documents tirés du Web à des fins de classification, recommandation, etc. en lien avec des partenaires industriels (e.g. DM2 digital marketing, MeetSys, Peer.Us).

Plus d'information sur Adrien Guille :

Séminaire DAPA du 9 / 7 / 2019 à 10h30

Big Data: An Imbalanced Learning Perspective

Haibo He (University of Rhode Island)

Lieu : LIP6, salle 405 (4ème étage), couloir 24-25, 4 place Jussieu, 75005 Paris

Big data has become an important topic worldwide over the past several years. Among many
aspects of the big data research and development, imbalanced learning has become a critical
component as many data sets in real-world applications are imbalanced, ranging from Internet,
finance, social network, to medical and health industry. In general, the imbalanced learning
problem is concerned with the performance of machine learning algorithms in the presence of
underrepresented data and severe class distribution skews. Due to the inherent complex
characteristics of imbalanced data sets, learning from such data requires new understandings,
principles, algorithms, and tools to transform vast amounts of raw data efficiently and
effectively into information and knowledge representation.

In this talk, I will start with an overview of the nature and foundation of the imbalanced
learning problem, and then focus on the state-of-the-art methods and technologies in dealing
with the imbalanced data, followed by a systematic discussion on the assessment metrics to
evaluate learning performance under the imbalanced learning scenario. I will also introduce the
latest research development in our group that we have developed and tested on various
imbalanced data sets. Finally, I will highlight the major opportunities and challenges, as well as
potential research directions for learning from imbalanced data facing the big data era.


Haibo He is a Fellow of IEEE and the Robert Haas Endowed Chair Professor at the University of
Rhode Island, Kingston, RI, USA. His primary research interests include computational intelligence
and various applications. He has published one sole-author book (Wiley), edited 1 book (Wiley-IEEE)
and 6 conference proceedings (Springer), and authored/co-authors over 300 peer-reviewed
journal and conference papers, including several highly cited papers in IEEE Transactions on Neural
Networks and IEEE Transactions on Knowledge and Data Engineering, Cover Page Highlighted
paper in IEEE Transactions on Information Forensics and Security, and Best Readings of the IEEE
Communications Society. He has delivered more than 80 invited talks around the globe. He was the
Chair of IEEE Computational Intelligence Society (CIS) Emergent Technologies Technical
Committee (ETTC) (2015) and the Chair of IEEE CIS Neural Networks Technical Committee
(NNTC) (2013 and 2014). He served as the General Chair of 2014 IEEE Symposium Series on
Computational Intelligence (IEEE SSCI’14, Orlando, Florida). He is currently the Editor-in-Chief of
IEEE Transactions on Neural Networks and Learning Systems. He was a recipient of the IEEE
International Conference on Communications (ICC) “Best Paper Award” (2014), IEEE CIS
“Outstanding Early Career Award” (2014), National Science Foundation “Faculty Early Career
Development (CAREER) Award” (2011), and Providence Business News (PBN) “Rising Star
Innovator” Award (2011).

Plus d'information sur Haibo He :

Séminaire DAPA du 6 / 6 / 2019 à 14h

Transparent modelling of uncertainty - a role for fuzzy sets and systems in XAI?

Christian Wagner (University of Nottingham)

Lieu : LIP6, salle 405 (4ème étage), couloir 24-25, 4 place Jussieu, 75005 Paris

While fuzzy sets and systems are often credited with being useful for modelling uncertainty and for being interpretable, it is straightforward to argue that they are neither. With both uncertainty handling and interpretability being 'hot topics' in AI, in this talk I reflect on the capacity of fuzzy sets and systems to transparently model and handle uncertainty, focussing in particular on data-driven fuzzy set design and novel approaches to non-singleton fuzzy logic systems.

In respect to the latter, I will discuss their utility in respect to 'performance' but also more generally in respect to the important reason they play in the systematic and transparent modelling of uncertainty affecting system inputs. Finally, time permitting, I will focus on how to capture uncertainty in the real world, presenting recent interdisciplinary work at the interface of computer and the social sciences.


Christian Wagner is an Associate Professor in Computer Science at the University of Nottingham, and founding director of the Lab for Uncertainty in Data and Decision Making (LUCID). He has published over 100 peer-reviewed articles, focussing on modelling & handling of uncertain data arising from heterogeneous data sources (e.g. stakeholders), with a particular emphasis on designing interpretable AI based decision support systems. In 2017, he was recognised as a RISE (Recognising Inspirational Scientists and Engineers) Connector by EPSRC. His work ranges from decision support in cyber security and environmental management to personalisation and control in manufacturing. He has led ten and co-led three research projects with partners from industry and government with an overall value of over £9.3m and co/developed multiple open source software frameworks, making cutting edge research accessible to research communities beyond computer science.

Dr Wagner is an Associate Editor of the IEEE Transactions on Fuzzy Systems journal, Chair of the IEEE CIS Technical Committee on Fuzzy Systems and Task Force on Cyber Security; elected member-at-large of the IEEE Computational Intelligence Society (CIS) Administrative Committee for 2018-2020. He is a General Chair of Fuzz-IEEE 2021 in Luxembourg and Special Sessions Chair at Fuzz-IEEE 2019 in New Orleans.

Plus d'information sur Christian Wagner :

Séminaire DAPA du 12 / 4 / 2019 à 14h30

On quantum probability for decision analysis

Hung. T Nguyen (New Mexico State University (USA) & Chiang Mai University (Thailand))

Lieu : LIP6, salle 405 (4ème étage), couloir 24-25, 4 place Jussieu, 75005 Paris

In view of current efforts on promoting the appropriate use of quantum probability (QP), mainly because of its non-commutativity property, in social decision problems, I will entertain the audience with an overview of its state-of-the-art.
Specifically, I will elaborate upon QP and its use in von Neumann's expected utility formalism, as a comparison with other well-known non-additive approaches.
If time permits, I will go a bit into Bohmian mechanics whose dynamics seems appropriate for modeling financial data exhibiting human factors.
These seemingly new ingredients in uncertainty analysis in general provide lots of important and "interesting" research programs ahead.


Doctorat d'Etat es Sciences Mathematiques, University of Lille, 1975.

Emeritus Professor of Mathematical Sciences, New Mexico State University (USA) and

Adjunct Professor of Economics, Chiang Mai University (Thailand).

Research interests: Fuzzy mathematics, random set theory, Conditional Event Algebra, Statistical estimation in non-regular models;

Currently: Quantum probability for decision theory and Statistics for Bohmian models in Financial Econometrics/ Predictive behavioral modeling in Econometrics.

Plus d'information sur Hung. T Nguyen :

Séminaire DAPA du 20 / 3 / 2019 à 10h

The Language of Betting as a Strategy for Scientific Communication

Glenn Shafer (Brunswick.Rutgers Business School – Newark and New Brunswick, New Jersey (USA))

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

The established language for statistical testing -- significance levels, power, and p-values -- is overly complicated and deceptively conclusive. Even teachers of statistics and scientists who use statistics misinterpret the results of statistical tests, tending to misstate their meaning and exaggerate their certainty. We can communicate the meaning and limitations of statistical evidence more clearly using the language of betting. This talk calls attention to a simple betting interpretation of likelihood ratios, significance levels, and p-values. This interpretation clarifies the special character of statistical testing and estimation, which should be only one chapter in a larger mathematical theory of evidence.


Glenn Shafer is professor at Rutgers University. In addition to being a university educator for nearly 50 years, he was a volunteer in the United States Peace Corps and served for four years as a business school dean. As a scholar, he is known for his work on the theory of evidence, on game-theoretic probability, and on the history of probability and statistics. His most recent book, Game-Theoretic Foundations for Probability and Finance with Volodya Vovk, will be published by Wiley in May.

Plus d'information sur Glenn Shafer :

Séminaire DAPA du 7 / 3 / 2019 à 10h30

Decomposable Probabilistic and Possibilistic Graphical Models: On Learning, Fusion and Revision

Rudolf Kruse (University of Magdeburg)

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

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.


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.

Plus d'information sur Rudolf Kruse :

Séminaire DAPA du 18 / 2 / 2019 à 13h

Techniques d'apprentissage pour la Perception (par la) Machine

Brahim Chaib-draa (Département d'informatique et de génie logiciel, Université de Laval, Canada)

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

La perception (par la) machine (Machine Perception) vise à concevoir des systèmes informatiques capables d'acquérir des percepts de l'environnement comme le ferait un humain lorsqu'il utilise ses sens pour être en lien avec son environnement. Dès lors de tels systèmes seraient capable de voir, de toucher, d'entendre, de sentir, etc.

Les travaux que je compte présenter dans ce séminaire se situent dans ce contexte et couvrent les aspects i) de reconnaissance de places; ii) de prise d'objets; iii) d'identification et de rangement d'objets, iv) de reconnaissance de texture par le toucher, et iii) d'évaluation de situation.

Pour chacun de ces aspects, je compte donner un aperçu des algorithmes d'apprentissage utilisés, les résultats expérimentaux obtenus ainsi que les travaux futurs envisagés.

Parallèlement à ces travaux, nous menons des recherches plus en lien avec l'apprentissage machine en particulier les aspects de généralisation, d'apprentissage multimodal et multitâche et d'utilisation des tenseurs. Si le temps le permet je donnerai un bref aperçu de ces recherches.


Brahim Chaib-draa a fait ses études d'ingénieur à Supélec (Paris) et a ensuite travaillé au Centre de recherche d'EDF Chatou. Il s'est ensuite orienté vers l'enseignement et la recherche après avoir obtenu un doctorat à Valenciennes. Depuis 1990, il est Professeur au département d'informatique et de génie logiciel à L'Université Laval (Québec-ville, Canada). Durant sa carrière il a effectué des séjours au DFKI (Allemagne), à L'INRIA et au LORIA (France), Chez AT&T (USA) et dernièrement à RIKEN (Japon). Il est membre sénior IEEE et membre de ACM.

Plus d'information sur Brahim Chaib-draa :

Séminaire DAPA du 13 / 9 / 2018 à 14h

Diurnal variations of psychometric indicators in Twitter content

Fabon Dzogang (Intelligent Systems Laboratory, University of Bristol, Bristol, UK)

Lieu : LIP6, salle 101 (1er étage), couloir 26-00, 4 place Jussieu, 75005 Paris

The psychological state of a person is characterised by cognitive and emotional variables which can be inferred by psychometric methods. Using the word lists from the Linguistic Inquiry and Word Count, designed to infer a range of psychological states from the word usage of a person, we studied temporal changes in the average expression of psychological traits in the general population. We sampled the contents of Twitter in the United Kingdom at hourly intervals for a period of four years, revealing a strong diurnal rhythm in most of the psychometric variables, and finding that two independent factors can explain 85% of the variance across their 24-h profiles. The first has peak expression time starting at 5am/6am, it correlates with measures of analytical thinking, with the language of drive (e.g power, and achievement), and personal concerns. It is anticorrelated with the language of negative affect and social concerns. The second factor has peak expression time starting at 3am/4am, it correlates with the language of existential concerns, and anticorrelates with expression of positive emotions. Overall, we see strong evidence that our language changes dramatically between night and day, reflecting changes in our concerns and underlying cognitive and emotional processes. These shifts occur at times associated with major changes in neural activity and hormonal levels.


Fabon obtained his PhD in Computer Science in 2013 “on Learning and Representation from Texts for both Emotional and dynamical Information” at the University of Pierre et Marie Curie, in the DAPA department at LIP6. After graduating he held a short post-doctoral position in LIP6, working on building interpretable models for the classification of multivariate time series’ data. At this time he grew an interest in the analysis of time series’ data, and in the Fourier transform as a mean to extract meaningful features from data. He later joined the University of Bristol as a research associate in 2014 where he worked on efficient machine learning algorithms for data streams, and developed tools to study our human behaviours at a collective level via the analysis of the social media and large samples of press archives. He combined his works on information dynamics and his interest in the study of emotions to research periodic patterns of emotions and mental health. His results provide evidence that a share of the variance in our collective behaviours and emotions are predictable across the year, and over the 24-h cycle.

Plus d'information sur Fabon Dzogang :

Séminaire DAPA du 28 / 6 / 2018 à 14h

A Cognitive Architecture for Object Recognition in Video

Jose C. Principe (Computational NeuroEngineering Lab, University of Florida)

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

This talk describes our efforts to abstract from the animal visual system the computational principles to explain images in video. We develop a hierarchical, distributed architecture of dynamical systems that self-organizes to explain the input imagery using an empirical Bayes criterion with sparseness constraints and dual state estimation. The interpretation of the images is mediated through causes that flow top down and change the priors for the bottom up processing. We will present preliminary results in several data sets.


Jose C. Principe (M’83-SM’90-F’00) is a Distinguished Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Florida where he teaches advanced signal processing, machine learning and artificial neural networks (ANNs) modeling. He is BellSouth Professor and the Founder and Director of the University of Florida Computational NeuroEngineering Laboratory (CNEL) . His primary area of interest is processing of time varying signals with adaptive neural models. The CNEL Lab has been studying signal and pattern recognition principles based on information theoretic criteria (entropy and mutual information).

Dr. Principe is an IEEE Fellow. He was the past Chair of the Technical Committee on Neural Networks of the IEEE Signal Processing Society, Past-President of the International Neural Network Society, and Past-Editor in Chief of the IEEE Transactions on Biomedical Engineering. He is a member of the Advisory Board of the University of Florida Brain Institute. Dr. Principe has more than 800 publications. He directed 92 Ph.D. dissertations and 65 Master theses. He wrote in 2000 an interactive electronic book entitled “Neural and Adaptive Systems” published by John Wiley and Sons and more recently co-authored several books on “Brain Machine Interface Engineering” Morgan and Claypool, “Information Theoretic Learning”, Springer, and “Kernel Adaptive Filtering”, Wiley.

Plus d'information sur Jose C. Principe :

Séminaire DAPA du 1 / 2 / 2018 à 10h

Machine Learning @dailymotion : Toward better content understanding and more accurate recommendation

Yves Mabiala (Dailymotion)

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

In this talk I will describe two of the main subjects the data science team at Dailymotion is focusing on. I will first start by describing how a video is automatically characterized in terms of verticals (sport, music, ...) and topics (coming from wikipedia) using multi-modal approaches based on the sound and the images of the video but also the text characterizing it. In a second step, I will describe how we are able to pick out of a 250 million video catalog the most accurate videos for millions of users especially using sequence models for session-based recommendation


Yves Mabiala is a data scientist leading the data science team at Dailymotion. He is currently working working on large scale recommendation problems and content characterization from raw signals (audio, video).
Prior to Dailymotion he was working at Thales as a research scientist in the data science lab where he was focusing on large scale unsupervised anomaly detection in cyber-security, credit card fraud detection or unsupervised sequence learning especially applied to predictive maintenance.
He was also a member of the LIP6/Thales joint lab, where he was working with the ComplexNetwork team on studying the dynamics of large graphs but also with MLIA team on time series representation learning.

Séminaire DAPA du 18 / 1 / 2018 à 10h30

Circadian Mood Variations in Twitter Content

Fabon Dzogang (Intelligent Systems Laboratory (ISL), University of Bristol, Bristol, UK)

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

Circadian regulation of sleep, cognition, and metabolic state is driven by a central clock, which is in turn entrained by environmental signals. Understanding the circadian regulation of mood, which is vital for coping with day-to-day needs, requires large datasets and has classically utilised subjective reporting. We use a massive dataset of over 800 million Twitter messages collected over the course of 4 years in the United Kingdom. We extract robust signals of the changes that happened during the course of the day in the collective expression of emotions and fatigue. We use methods of statistical analysis and Fourier analysis to identify periodic structures, extrema, change-points, and compare the stability of these events across seasons and weekends. We reveal strong, but different, circadian patterns for positive and negative moods. The cycles of fatigue and anger appear remarkably stable across seasons and weekend/weekday boundaries. Positive mood and sadness interact more in response to these changing conditions. Anger and, to a lower extent, fatigue show a pattern that inversely mirrors the known circadian variation of plasma cortisol concentrations. Most quantities show a strong inflexion in the morning. Since circadian rhythm and sleep disorders have been reported across the whole spectrum of mood disorders, we suggest that analysis of social media could provide a valuable resource to the understanding of mental disorder.


Fabon defended his PhD thesis in Computer Science in 2013 “on Learning and Representation from Texts for both Emotional and dynamical Information” at the University of Pierre et Marie Curie, in the DAPA department at LIP6. After graduating he held a short post-doctoral position in LIP6, working on building interpretable models for the classification of multivariate time series’ data. At this time he grew an interest in the analysis of time series’ data, and in the Fourier transform as a mean to extract meaningful features from data. He later joined the University of Bristol as a research associate in 2014 where he worked on efficient machine learning algorithms for data streams, and developed tools to study our human behaviours at a collective level via the analysis of the social media and large samples of press archives. He combined his works on information dynamics and his interest in the study of emotions to research periodic patterns of emotions and mental health. His results provide evidence that a share of the variance in our collective behaviours and emotions are predictable across the year, and over the 24-h cycle.

Plus d'information sur Fabon Dzogang :

Séminaire DAPA du 29 / 11 / 2017 à 14h

Exploring the Trade-Offs of Web Interfaces to Support Live Queries over (Semantic) Web Data

Olaf Hartig (Linköping University)

Lieu : LIP6, salle 405 (4ème étage), couloir 24-25, 4 place Jussieu, 75005 Paris

In the context of the Linked Open Data effort, a significant number
of public SPARQL endpoints had been made available on the Web to provide
query-based access to various types of datasets. Many such endpoints have
sacrificed high availability because maintaining a server that provides a
reliable SPARQL endpoint is costly. To address this issue we have started
investigating approaches that shift some of the effort of executing queries
from the server to the clients; these approaches rely only on data access
interfaces that are limited to simple types of requests. In this two-parts
talk I will first introduce two such interfaces and present experimental
results that highlight their respective properties. Thereafter, in the second
part of the talk, I will introduce an abstract machine model that allows us to
study such client-server scenarios formally. I will present results of such a
study based on which we have drawn a fairly complete expressiveness lattice
that shows the interplay between several combinations of client and server
capabilities. Additionally, I will show the usefulness of our model to
formally analyze the fine-grain interplay between several metrics such as the
number of requests sent to the server, and the bandwidth of communication
between client and server.


Olaf is an Assistant Professor at the Department of Computer and
Information Science of Linköping University. He holds a Ph.D. in Computer
Science from the Humboldt-Universität zu Berlin, and worked previously as a
postdoctoral research fellow at the Cheriton School of Computer Science at the
University of Waterloo and, thereafter, at the Hasso Plattner Institute,
Potsdam. Olaf is interested in problems related to the management of data and
databases. His focus in this broad context is on data on the Web and on graph
data, as well as on problems in which the data is distributed over multiple,
autonomous and/or heterogeneous sources. Regarding these topics, Olaf's
interests range from systems-building related research (e.g., efficient storage
of data, query processing, and query optimization) all the way to theoretical
foundations (e.g., complexity and expressive power of query languages). Olaf
was honored with the SWSA Distinguished Dissertation Award in 2015 for his
Ph.D. dissertation “Querying a Web of Linked Data: Foundations and Query
Execution,” and he has received two best research paper awards (ESWC 2009 and
ESWC 2015). Olaf is leader or contributor of several open source projects,
most notably SQUIN, which is a novel query processing system for the Semantic
Web. He co-organized international research workshops, served on multiple
program committees, and participated as an invited expert in the provenance
incubator group and the provenance working group of the World Wide Web

Plus d'information sur Olaf Hartig :

Séminaire DAPA du 2 / 10 / 2017 à 13h

Apprentissage profond et génération de musique

Jean-Pierre Briot (LIP6, UPMC)

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

L’apprentissage profond s’est imposé dans le paysage de l’apprentissage machine à base de données avec des applications à large échelle en matière de reconnaissance d’image, vocale et de traduction. Du fait de son ADN hérité des réseaux de neurones artificiels et de la régression linéaire, il est de manière naturelle très approprié pour des tâches de prédiction et de classification. De récents travaux portent sur son application à la génération de contenu, images, texte et musique, en bénéficiant des capacités d’apprentissage de corpus et ainsi de style. Des enjeux actuels sont la capacité d’imposer des contraintes globales sur la génération (ex : tonalité, structure…) ainsi que de favoriser l’originalité des contenus générés, ce qui n’est pas l’objectif premier de l’apprentissage profond. Nous présenterons ici diverses approches, identifiées à partir de l’analyse de nombreux articles scientifiques et travaux récents dans ce domaine très actif, telles : le contrôle de la génération d’échantillons (sampling), la manipulation de données d’entrée, les architectures génératives adversaires (GAN), l’apprentissage par renforcement et la sélection et la concaténation d’unités musicales. Nous présenterons quelques exemples représentatifs de telles approches.

Cet exposé se base sur le récent pré-ouvrage sur le sujet, en collaboration avec Gaëtan Hadjeres et François Pachet :


Jean-Pierre Briot est Directeur de recherche CNRS, membre du LIP6, au sein de l’équipe SMA dans le Département DESIR. Ayant principalement travaillé sur des modèles de programmation et de conception de logiciel adaptatif et coopératif (objets, acteurs concurrents, composants répartis, agents), il s’est récemment ré-intéressé à l’informatique musicale, entamée lors de sa thèse entre l’IRCAM et le LITP (un des laboratoires fondateur du LIP6) au milieu des années 80 et également récemment intéressé au phénomène de l’apprentissage profond.

Plus d'information sur Jean-Pierre Briot :

Séminaire DAPA du 4 / 7 / 2017 à 10h

Big Data Analytics using Deep Learning and Information Theoretical Learning: Applications to Astronomy

Pablo A. Estévez (Department of Electrical Engineering, University of Chile, and Millennium Institute of Astrophysics, Chile)

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

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.


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.

Plus d'information sur Pablo A. Estévez :

Séminaire DAPA du 4 / 5 / 2017 à 10h

Multi-Criteria Decision Making and Uncertainty

Ronald R. Yager (Machine Intelligence Institute, Iona College, New Rochelle, NY, USA)

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

Multi-Criteria aggregation is a pervasive problem appearing in many technological domains. During this presentation we shall discuss some issue 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.


Ronald R. Yager is Director of the Machine Intelligence Institute and Professor of Information Systems at Iona College. He is editor and 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 Intelligent 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 International the 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.

Plus d'information sur Ronald R. Yager :

Séminaire DAPA du 28 / 3 / 2017 à 11h

Quelques Résultats Récents dans le Domaine des Systèmes Robotiques Distribués Intelligents

Didier El Baz (Laboratoire d'analyse et d'architecture des systèmes)

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

Dans cet exposé nous présentons nos travaux de recherche dans le domaine des systèmes robotiques distribués intelligents. Nous présentons notamment les travaux effectués dans le cadre des projets ANR Smart Surface et Smart Blocks qui ont porté sur la conception et la fabrication de convoyeurs distribués reconfigurables. En particulier, nous détaillons les aspects relatifs à l'algorithmique distribuée
pour la reconnaissance des pièces sur un convoyeur distribué et pour le déplacement des blocs.
Nous concluons par de nouveaux résultats sur la conception des blocs et de leurs moteurs linéaires.


Le Dr. Didier El Baz est ingénieur diplômé en Génie Electrique de l’INSA de Toulouse (1981). Didier El Baz est Docteur Ingénieur en Automatique de l’INSA de Toulouse (1984), diplômé du Programme d’Eté Data Networks du MIT, USA, 1984 et a reçu l’Habilitation à Diriger des Recherches de l’Institut National Polytechnique de Toulouse en 1998. Didier El Baz a été Stagiaire Postdoctoral INRIA au Laboratory for Information and Decision Systems du MIT, USA, de mars 1984 à février 1985.

Didier El Baz est Chercheur CNRS, fondateur et responsable au LAAS-CNRS de l’équipe Calcul Distribué et Asynchronisme, CDA. Didier El Baz a été le porteur et le coordonnateur du projet ANR Calcul intensif pair à pair (ANR-07-CIS7-011) qui a commencé en 2008 et s’est achevé en 2011.

Les domaines de recherche de Didier El Baz concernent le calcul intensif, le calcul distribué, la conception et l’analyse d’algorithmes parallèles ou distribués, les itérations asynchrones. Les applications traitées vont de la commande optimale, à la résolution d’équations aux dérivées partielles discrétisées en passant par l’optimisation non linéaire, l’optimisation combinatoire et la robotique. Didier El Baz est l’auteur de quarante articles dans des revues scientifiques internationales et de soixante-dix articles dans des conférences internationales avec actes. Il a dirigé onze thèses de Doctorat.

Didier El Baz est membre du Comité de Programme de la Conférence Parallel Distributed and network-based Processing depuis 2003. Il membre du Steering Commitee de PDP depuis 2008. Didier El Baz a été Président du Comité de Programme de la conférence PDP en 2008 et 2009 et Président du Comité d’Organisation de PDP en 2008. Il a été General co-Chairman de la conférence internationale IEEE iThings 2013, Pékin après avoir été Workshops Chairman de IEEE iThings en 2012 à Besançon. Il a été Chairman du Comité de Programme de seize workshops Internationaux sur le calcul parallèle et distribué notamment en liaison avec des Symposiums comme IEEE IPDPS. Le Dr Ingénieur Didier El Baz a été Général Chairman de la 16ème conférence IEEE Scalable Computing and Communications, de la Conférence IEEE Cloud and Big Data Computing de la treizième conférence IEEE Ubiquitous Intelligence and Computing ainsi