368 citations · 736 across the 32 of their papers we have counts for
4 papers · 1 filter
PreDiff: Precipitation Nowcasting with Latent Diffusion Models
Zhihan Gao, Xingjian Shi, Boran Han +6
Earth system forecasting has traditionally relied on complex physical models that are computationally expensive and require significant domain expertise. In the past decade, the un…
Theoretical Guarantees of Learning Ensembling Strategies with Applications to Time Series Forecasting
Hilaf Hasson, Danielle C. Maddix, Yuyang Wang +2
Ensembling is among the most popular tools in machine learning (ML) due to its effectiveness in minimizing variance and thus improving generalization. Most ensembling methods for b…
Cross-Frequency Time Series Meta-Forecasting
Mike Van Ness, Huibin Shen, Hao Wang +3
Meta-forecasting is a newly emerging field which combines meta-learning and time series forecasting. The goal of meta-forecasting is to train over a collection of source time serie…
Learning Physical Models that Can Respect Conservation Laws
Derek Hansen, Danielle C. Maddix, Shima Alizadeh +2
Recent work in scientific machine learning (SciML) has focused on incorporating partial differential equation (PDE) information into the learning process. Much of this work has foc…