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Deep Unsupervised Learning for Climate Informatics with Claire Monteleoni - #497

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

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Unsupervised Downscaling Techniques in Climate Informatics

This chapter explores an unsupervised method for downscaling climate data, highlighting its advantages over traditional AI techniques like GANs. It emphasizes the use of normalizing flows for more nuanced environmental data representation and discusses its application in generating detailed precipitation maps from coarse-scale temperature data.

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