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Found 5 papers, 3 papers with code
AlphaPre: Amplitude-Phase Disentanglement Model for Precipitation Nowcasting
Inspired by the fact that in the frequency domain, phase variations are shown to correspond to changes in the position of precipitation, while amplitude variations are linked to intensity changes, we propose an amplitude-phase disentanglement model called AlphaPre, which separately learn the position and intensity changes of precipitation.
Four-hour thunderstorm nowcasting using deep diffusion models of satellite
In terms of application, our system operates efficiently (forecasting 4 hours of convection in 8 minutes), and is highly transferable with the potential to collaborate with multiple satellites for global convection nowcasting.
DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting
A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion, which effectively tackles the shortcomings of previous methods.
MetaDiff: Meta-Learning with Conditional Diffusion for Few-Shot Learning
Its key idea is learning a deep model in a bi-level optimization manner, where the outer-loop process learns a shared gradient descent algorithm (i. e., its hyperparameters), while the inner-loop process leverage it to optimize a task-specific model by using only few labeled data.
A Personalized Utterance Style (PUS) based Dialogue Strategy for Efficient Service Requirement Elicitation
Considering that traditional dialogue system with static slots cannot be directly applied to the SRE task, it is a challenge to design an efficient dialogue strategy to guide users to express their complete and accurate requirements in such a huge potential requirement space.
