Deep Unsupervised Learning for Climate Informatics

For slides and more information on the paper, visit 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.