Jeudi 3 Octobre
Heure: 
12:15  13:30 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
Optimal Transport for Machine Learning 
Description: 
Bernard KamsuFoguem 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, multilabel 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 LargeScale 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 multimodal 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 multimodal sensor data analysis, pattern mining and graph analytics. 

