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- W4387389849 abstract "Machine learning traditionally assumes that training and testing data are distributed independently and identically. However, in many real-world settings, the data distribution can shift over time, leading to poor generalization of trained models in future time periods. Our paper presents a novel prompting-based approach to temporal domain generalization that is parameter-efficient, time-efficient, and does not require access to the target domain data (i.e., unseen future time periods) during training. Our method adapts a target pre-trained model to temporal drift by learning global prompts, domain-specific prompts, and drift-aware prompts that capture underlying temporal dynamics. It is compatible across diverse tasks, such as classification, regression, and time series forecasting, and sets a new state-of-the-art benchmark in temporal domain generalization. The code repository will be publicly shared." @default.
- W4387389849 created "2023-10-06" @default.
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- W4387389849 date "2023-10-03" @default.
- W4387389849 modified "2023-10-07" @default.
- W4387389849 title "Prompting-based Efficient Temporal Domain Generalization" @default.
- W4387389849 doi "https://doi.org/10.48550/arxiv.2310.02473" @default.
- W4387389849 hasPublicationYear "2023" @default.
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