La thématique de cette année est l’interaction entre les mathématiques et l’apprentissage automatique. En particulier nous nous intéressons à la reconversion des mathématiciens dans l’intelligence artificielle (IA). Nous avons donc invité des mathématiciens ayant basculé dans l’apprentissage automatique/IA/Science des données pour expliquer leur nouvelles thématiques. L'intérêt est triple:
un nombre non négligeable des étudiants que nous avons formés (en thèse en particulier) se sont réorientés dans ce domaine étant donné la difficulté à trouver un poste. Une telle conférence leur permet d'expliquer en quoi leur formation est utile pour une telle carrière et quels sont les besoins en mathématiques qu'ils voient émerger dans ces domaines.
Probablement que des nouveaux étudiants seront confrontés à un tel choix de carrière. Il est donc utile pour eux d'en savoir plus sur ces options et de pouvoir échanger avec des anciens qui sont passés par là et pourront les conseiller ou bien même les aider dans le futur à intégrer les entreprises de leur réseau professionnel.
Faire le point sur un domaine en pleine expansion et qui utilise très fortement des mathématiques avancées, pouvant conduire à des collaborations/nouvelles directions de recherche.
Lundi :
12:30 Déjeuner à l'atelier Marc Meurin
14:00 Mihaly Petreczky
15:00 Benoit Dherin (visio-conférence)
Mardi :
09:30 François Petit
10:30 Gennaro di Brino (visio-conférence)
11:30 Mathieu Klimczak
12:30 Déjeuner à l'Ardoise (formulaire)
14:00 Nicolas Ricka
15:00 Thibault Defourneau
All talks (given in person or remotely) will be on zoom at the link:
https://univ-artois-fr.zoom.us/j/92303799154
Topic: Journees Lensoises
Time: Jul 12, 2021 01:45 PM Paris
Meeting ID: 923 0379 9154
Les exposés seront donné dans l'amphi S25. Pour y accéder, à partir de l'entrée principale du bâtiment B, descendre au sous-sol, ce sera l'amphi le plus à droite.
Mathieu Klimczak :
I did a PhD in algebraic topology, and then all of a sudden I was advertised as an "expert in Artificial Intelligence". The idea of this talk will be to briefly present the company I work at (CITC-EuraRFID), and the changes I noticed by switching from academia to the private sector. I will then use most of my time to talk about 2 uses cases on embedded computer vision I had to work on.
Thibault Defourneau :
The main idea of this talk is to share my experience from Mathematics to Data science, and to explain the challenges I faced in the digital word. First, I will present briefly Trinov, the company I am working for, as well as main solutions developed by exploiting data. Then, I will focus on one specific solution I develop by explaining some key concepts and techniques behind it.
Benoit Dherin
Title: Implicit Gradient Regularization
Abstract: Large deep neural networks used in modern supervised learning have a large submanifold of interpolating solutions, most of which are not good. However, it has been observed experimentally that gradient descent tends to converge in the vicinity of flat interpolating solutions producing trained models that generalize well to new data points, and the more so as the learning rate increases. Using backward error analysis, we will show that gradient descent actually follows the exact gradient flow of a modified loss surface, which can be described by a regularized loss preferring optimization paths with shallow slopes, and in which the learning rate plays the role of a regularization rate. (This is joint work with David Barrett from DeepMind).
Gennaro Di Brino
Title: An incomplete survey of recommender systems
Abstract: We will give a high level overview of the kind of models that do the heavy lifting in most of the major tech companies, by suggesting items, content, movies, etc.. to users. In doing so, we will mention some interesting techniques that transformed the field of Data Science, and some other very promising ones. The focus will be on the shift of perspective it takes to view yet the same mathematical object or even the same model architecture in a completely different context, where it can be extremely effective.
Mihaly Petreczky (CNRS, Ecole Centrale Lille, research group CIRStAL).
Title: Control theory and machine learning for dynamical systems.
Abstract: In this talk I will present the relationship between control theory and learning dynamical systems, and I will present some mathematical problems
which arise in this setting. The goal of control theory is to develop algorithms for influencing the behavior of dynamical systems. To this end,
models of dynamical systems are required. In turn models are often derived from measurement data.
The branch of control theory which studies the estimation of
models from dynamical systems using data is known as system identification. The goal of machine learning is to build models from data. In particular,
many popular model classes (recurrent neural networks, LSTM) used in machine learning are in fact models of dynamical systems.
Hence, the corresponding theoretical challenges are the same as in system identification. In particular, one of the theoretical tools to prove
properties of learning algorithms is realization theory, which addresses the relationship between the input-output behavior of the model and its
internal structure. In turn, realization theory gives rise to several interesting mathematical problems which are interesting on their own right.
Nicolas Ricka
Title: Topological analysis for mental health prediction
Abstract: In this talk, we will motivate the use of machine learning to study certain aspects of mental health. In the specific case of major depressive disorder and post traumatic stress disorder, we will discuss the advantages of using topological invariants to help the clinician in assessing the patients, and describe some results in this direction.
Ivo Dell'Ambrogio (Lille) m
Thierry Bay (UPHF) m
Ouriel Bloede (Angers)m
Benoit Brebion lm
Thibault Defourneau (Trinov) l
Benoit Dhérin (Google)
Gennaro Di Brino (Come)
Naim Es Sebani
Yaël Frégier (Artois) lm
Matthieu Goliot lm
Martin Gonzalez
Jean-Baptiste Gouray (Artois) lm
Arthur Kipfel (Artois) lm
Mathieu Klimcak (Eurafid) lm
Anthony Pereira lm
Olivier Peltre (Artois)
Mihaly Petreczky (CNRS) l
Camilla Penzo (Artois) lm
François Petit (Paris) m
Germain Poloudenny l
Nicolas Ricka p
Martin Saralegi (Artois) lm
Arthur Soulié (Glasgow)
Daniel Tanré (Lille)
LML - Laboratoire de Mathématiques de Lens
http://lml.univ-artois.fr/public/francais/index.html
Faculté des Sciences Jean Perrin, Université d'Artois
Rue Jean Souvraz SP 18, 62307 LENS CEDEX
Nous avons des fonds pour financer partiellement votre séjour
Ivo Dell'Ambroggio, Yaël Fregier, Camilla Penzo, Martin Saralegi
Contacter camilla.penzo@univ-artois.fr
Nous remercions la fédération de mathématiques du Nord ainsi que le laboratoire de mathématiques de Lens pour leur soutien financier.