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Found 4 papers, 4 papers with code
A spatiotemporal machine learning approach to forecasting COVID-19 incidence at the county level in the USA
We highlight that the underutilization of data-driven forecasting of disease spread prior to COVID-19 is likely due to the lack of sufficient data available for previous diseases, in addition to the recent advances in machine learning methods for spatiotemporal forecasting.
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.
InceptionTime: Finding AlexNet for Time Series Classification
TSC is the area of machine learning tasked with the categorization (or labelling) of time series.
Proximity Forest: An effective and scalable distance-based classifier for time series
We demonstrate on a 1M time series Earth observation dataset that Proximity Forest retains this accuracy on datasets that are many orders of magnitude greater than those in the UCR repository, while learning its models at least 100, 000 times faster than current state of the art models Elastic Ensemble and COTE.
