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Inference from Evolving Populations: Agriculture

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TGMW89 - Unlocking Data Streams

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.

This talk is part of the Isaac Newton Institute Seminar Series series.

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