1 citations · 1 across the 6 of their papers we have counts for
4 papers · 1 filter
Hierarchical Graph Networks for Accurate Weather Forecasting via Lightweight Training
Thomas Bailie, S. Karthik Mukkavilli, Varvara Vetrova +1
Climate events arise from intricate, multivariate dynamics governed by global-scale drivers, profoundly impacting food, energy, and infrastructure. Yet, accurate weather prediction…
Reducing Smoothness with Expressive Memory Enhanced Hierarchical Graph Neural Networks
Thomas Bailie, Yun Sing Koh, S. Karthik Mukkavilli +1
Graphical forecasting models learn the structure of time series data via projecting onto a graph, with recent techniques capturing spatial-temporal associations between variables v…
Diving Deep: Forecasting Sea Surface Temperatures and Anomalies
Ding Ning, Varvara Vetrova, Karin R. Bryan +4
This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Princ…
HoGA: Higher-Order Graph Attention via Diversity-Aware k-Hop Sampling
Thomas Bailie, Yun Sing Koh, Karthik Mukkavilli
Graphs model latent variable relationships in many real-world systems, and Message Passing Neural Networks (MPNNs) are widely used to learn such structures for downstream tasks. Wh…