ML for Visualization
This project explores how autoencoders can improve the visualization of spatial ensembles by extracting meaningful features before applying dimensionality reduction techniques like UMAP. The goal is to create more expressive 2D projections that effectively capture spatial structures and spatio-temporal behaviors in ensemble data.
Oct 4, 2021
Evaluation and Selection of Autoencoders for Expressive Dimensionality Reduction of Spatial Ensembles
Oct 4, 2021