Chair: Jean-Michel Poggi (Univ. Paris-Saclay)
In recent years, significant progress has been made in the implementation of decision support systems based on machine learning methods by exploiting very large databases and the use of learning algorithms.
In many research or production environments, the available databases are rarely as large, and the question arises as to whether it makes sense to use machine learning methods in this context.
The ENBIS webinar will introduce transfer learning, which uses knowledge from related applications to implement efficient models with an economy of data.
Several achievements will be presented that successfully use these learning approaches to design machine learning for industrial small data regimes and to develop powerful decision support tools even in cases where the initial data volume is limited.
A de Mathelin, F Deheeger, M Mougeot, N Vayatis
From Theoretical to Practical Transfer Learning: The ADAPT Library
Federated and Transfer Learning, Springer 2022
Mathilde Mougeot is Professeur of Data Science at Ecole Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ENSIIE) and adjunct Professor at ENS Paris Saclay where she holds the Industrial Research Chair "Industrial Data Analytics & Machine Learning". Since the beginning of her career, Mathilde Mougeot has been interested in machine learning for artificial intelligence applications. Her research activity is motivated by questions related to concrete applications stemming from collaborative projects with the socio-economic world. Her research focuses mainly on scientific issues related to predictive models in various contexts, such as those of high dimensionality, model aggregation, domain adaptation, data frugality by model transfer or by hybrid models.
From 1999 to 2005, she has been contributed to the creation and the development of the start-up Miriad Technologies, specialized in the development of mathematical solutions for the industry based on machine learning, statistics and signal processing techniques.
She offers a strong experience in leading projects at the interface of academics and industry.