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Jeudi 3 Octobre
Heure: 12:15 - 13:30
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Optimal Transport for Machine Learning
Description: Bernard Kamsu-Foguem Optimal transport defines a set of geometric tools with interesting properties (comparison and morphology of probability measurements) that make it particularly suitable for solving large scale artificial learning problems.
Since probability distributions are omnipresent in Machine Learning (ML), whether theoretical or empirical, optimal transport can be useful in order to measure their separation or, even better, to be able to transform them at a lower cost.
We will investigate techniques based on optimal transport in certain practical and theoretical contexts (text classification, multi-label classification and domain adaptation), to contribute to solving the associated Machine Learning (ML) problems.
Lundi 7 Octobre
Heure: 12:30 - 14:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Analysing Large-Scale Research Data with Semantic Technologies - one year after
Description: Francesco OSBORNE
Jeudi 17 Octobre
Heure: 14:00 - 16:00
Lieu: Salle B107, bâtiment B, Université de Villetaneuse
Résumé: Explicative Data Analytics
Description: Martin Atzmüller Modeling and mining multi-modal and heterogeneous data is important in the context of analyzing knowledge and information processes in complex environments, e.g. for mining high dimensional and heterogeneous (sensor) data, the analysis of exceptional patterns, and complex network structures. For making sense of the data, explicative data analytics focuses on interpretable, transparent and explainable approaches, which is relevant for very many applications for analyzing data in science and industry. The talk presents according approaches for explicative data analytics incorporating methods from data science, machine learning, and human computing, exemplified by multi-modal sensor data analysis, pattern mining and graph analytics.