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pro vyhledávání: '"Xu, Wanghan"'
The development of Time-Series Forecasting (TSF) techniques is often hindered by the lack of comprehensive datasets. This is particularly problematic for time-series weather forecasting, where commonly used datasets suffer from significant limitation
Externí odkaz:
http://arxiv.org/abs/2406.14399
Data-driven artificial intelligence (AI) models have made significant advancements in weather forecasting, particularly in medium-range and nowcasting. However, most data-driven weather forecasting models are black-box systems that focus on learning
Externí odkaz:
http://arxiv.org/abs/2405.13796
The advent of data-driven weather forecasting models, which learn from hundreds of terabytes (TB) of reanalysis data, has significantly advanced forecasting capabilities. However, the substantial costs associated with data storage and transmission pr
Externí odkaz:
http://arxiv.org/abs/2405.03376
In recent years, with the rapid development of graph neural networks (GNN), more and more graph datasets have been published for GNN tasks. However, when an upstream data owner publishes graph data, there are often many privacy concerns, because many
Externí odkaz:
http://arxiv.org/abs/2403.00030
Autor:
Gong, Junchao, Bai, Lei, Ye, Peng, Xu, Wanghan, Liu, Na, Dai, Jianhua, Yang, Xiaokang, Ouyang, Wanli
Precipitation nowcasting based on radar data plays a crucial role in extreme weather prediction and has broad implications for disaster management. Despite progresses have been made based on deep learning, two key challenges of precipitation nowcasti
Externí odkaz:
http://arxiv.org/abs/2402.04290
Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models. However, most of these ML
Externí odkaz:
http://arxiv.org/abs/2402.01295
Autor:
Xu, Wanghan
The problem of graph isomorphism is an important but challenging problem in the field of graph analysis, for example: analyzing the similarity of two chemical molecules, or studying the expressive ability of graph neural networks. WL test is a method
Externí odkaz:
http://arxiv.org/abs/2402.08429
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