IMB > Recherche > Séminaires

Le Colloquium

  • Le 14 février 2019 à 15:30
  • Salle 2
    Romain Couillet
    Random Matrix Advances in Machine Learning
    Machine learning algorithms, starting from elementary yet popular ones, are difficult to theoretically analyze as (i) they are data-driven, and (ii) they rely on non-linear tools (kernels, activation functions). These theoretical limitations are exacerbated in large dimensional datasets where standard algorithms behave quite differently than predicted, if not completely fail. In this talk, we will show how random matrix theory (RMT) answers all these problems. We will precisely show that RMT provides a new understanding and various directions of improvements for kernel methods, semi-supervised learning, SVMs, community detection on graphs, spectral clustering, etc. Besides, we will show that RMT can explain observations made on real complex datasets in as advanced methods as deep neural networks.
  • Le 21 mars 2019 à 15:30
  • Salle de Conférences
    Glenn Webb
    Spatial Spread of Epidemic Diseases in Geographical Settings: Seasonal Influenza Epidemics in Puerto Rico
    Deterministic models are developed for the spatial spread of epidemic diseases in geographical settings. The models are focused on outbreaks that arise from a small number of infected hosts imported into sub-regions of the geographical settings. The goal is to understand how spatial heterogeneity influences thetransmission dynamics of the susceptible and infected populations. The models consist of systems of partial differential equations with diffusion terms describing the spatial spread of the underlying microbial infectious agents. The model is compared with real data from seasonal influenza epidemics in Puerto Rico. Joint work with Pierre Magal and Yixiang Wu.

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