Deep Unsupervised Learning for Climate Informatics

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Speaker: Claire Monteleoni; Host: Andre Erler

Motivation:
Prediction of Global Climate Change is an important problem for adaptation, but Global Climate Models still have many errors/biases and the resolution is too low for impact modeling.
At the same time, bias-correction and "downscaling" (upsampling) can be framed as classic ML problems.
  1. LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Комментарии
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This was extremely hopeful. I truly appreciate the considerations for predictive modeling and weather from cross discipline expressions. Thank you.

notsure
Автор

The loss presented at 46:19 seems similar to the “cycle consistency loss” in CycleGANs . Guessing there’s a connection, not clear about the exact connection between the normalizing flow approaches presented in this talk and CycleGANs.

nikhilm