104 citations · 148 across the 34 of their papers we have counts for
5 papers · 1 filter
Self-supervised Spatial-Temporal Learner for Precipitation Nowcasting
Haotian Li, Arno Siebes, Siamak Mehrkanoon
Nowcasting, the short-term prediction of weather, is essential for making timely and weather-dependent decisions. Specifically, precipitation nowcasting aims to predict precipitati…
Dual Stream Graph Transformer Fusion Networks for Enhanced Brain Decoding
Lucas Goene, Siamak Mehrkanoon
This paper presents the novel Dual Stream Graph-Transformer Fusion (DS-GTF) architecture designed specifically for classifying task-based Magnetoencephalography (MEG) data. In the…
Data-Efficient Sleep Staging with Synthetic Time Series Pretraining
Niklas Grieger, Siamak Mehrkanoon, Stephan Bialonski
Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small data…
GA-SmaAt-GNet: Generative Adversarial Small Attention GNet for Extreme Precipitation Nowcasting
Eloy Reulen, Siamak Mehrkanoon
In recent years, data-driven modeling approaches have gained significant attention across various meteorological applications, particularly in weather forecasting. However, these m…
Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting
Lorand Vatamany, Siamak Mehrkanoon
Accurate precipitation nowcasting is crucial for applications such as flood prediction, disaster management, agriculture optimization, and transportation management. While many stu…