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Presented by: 
Maud Lemercier (University of Warwick, The Alan Turing Institute)
Tuesday, March 16, 2021 - 11:00 to 11:25
INI Seminar Room 1

Inferring properties about time-evolving populations is a widespread problem, yet a non-standard machine learning task. Most existing machine learning models can either handle a static snapshot of a population or a single trajectory. In this talk I will present a generic framework, based on the expected signature which enables to compactly summarize a cloud of time series and make decisions on it. I will discuss an application in agricultural monitoring, where a key challenge is to predict the yield before harvest using a collection of time series acquired by satellite-sensors.