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Conference Paper: Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond

TitleCurse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond
Authors
Ke, YekunLiang, YingyuShi, ZhenmeiZhao, SongYang, Chiwun
Issue Date24-Mar-2025
Persistent Identifierhttp://hdl.handle.net/10722/359533

 

DC FieldValueLanguage
dc.contributor.authorKe, Yekun-
dc.contributor.authorLiang, Yingyu-
dc.contributor.authorShi, Zhenmei-
dc.contributor.authorZhao, Song-
dc.contributor.authorYang, Chiwun-
dc.date.accessioned2025-09-07T00:30:57Z-
dc.date.available2025-09-07T00:30:57Z-
dc.date.issued2025-03-24-
dc.identifier.urihttp://hdl.handle.net/10722/359533-
dc.languageeng-
dc.relation.ispartofConference on Parsimony and Learning 2025 (24/03/2025-27/03/2025, Stanford University, California)-
dc.titleCurse of Attention: A Kernel-Based Perspective for Why Transformers Fail to Generalize on Time Series Forecasting and Beyond-
dc.typeConference_Paper-

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