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, biascorrection and downscaling (upsampling) can be framed as classic ML problems.