1 citations · 2 across the 6 of their papers we have counts for
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Predicting the Future by Retrieving the Past
Dazhao Du, Tao Han, Song Guo
Deep learning models such as MLP, Transformer, and TCN have achieved remarkable success in univariate time series forecasting, typically relying on sliding window samples from hist…
STCast: Adaptive Boundary Alignment for Global and Regional Weather Forecasting
Hao Chen, Tao Han, Jie Zhang +2
To gain finer regional forecasts, many works have explored the regional integration from the global atmosphere, e.g., by solving boundary equations in physics-based methods or crop…
CRA5: Extreme Compression of ERA5 for Portable Global Climate and Weather Research via an Efficient Variational Transformer
Tao Han, Zhenghao Chen, Song Guo +2
The advent of data-driven weather forecasting models, which learn from hundreds of terabytes (TB) of reanalysis data, has significantly advanced forecasting capabilities. However,…
DiPrompT: Disentangled Prompt Tuning for Multiple Latent Domain Generalization in Federated Learning
Sikai Bai, Jie Zhang, Shuaicheng Li +5
Federated learning (FL) has emerged as a powerful paradigm for learning from decentralized data, and federated domain generalization further considers the test dataset (target doma…