Mercoledì 12 aprile alle 14. 30 si tiene il seminario di Gabriel Peyrè all'interno del ciclo AIM. Gabriel è professore al dipartimento di matematica e applicazioni dell'École normale supérieure a Parigi.
Il titolo del talk è Scaling Optimal Transport for High Dimensional Learning
Di seguito l'abstract.
"Scaling Optimal Transport for High Dimensional LearningAbstract: Optimal transport (OT) has recently gained a lot of interest in machine learning. It is a natural tool to compare in a geometrically faithful way probability distributions. It finds applications in both supervised learning (using geometric loss functions) and unsupervised learning (to perform generative model fitting). OT is however plagued by the curse of dimensionality since it might require a number of samples which grows exponentially with the dimension. In this talk, I will explain how to leverage entropic regularization methods to define computationally efficient loss functions, approximating OT with a better sample complexity. More information and references can be found on the website of our book"Computational Optimal Transport" https://optimaltransport.github.io/”.
Il seminario sarà trasmesso in streaming sul canale Facebook @ist.applicazionidelcalcolo e sul canale YouTube CNR IAC.
Qui il programma completo dei seminari.
