Exhilarate
发表于 2025-3-28 14:34:19
Meeta Banerjee,Jacquelynne S. Ecclesnot consider time series similarities during scenario reduction, a wind power day-ahead scenario analysis method based on ICGAN and IDTW-Kmedoids is proposed. First, introducing a multi-time scale convolution layer into the CGAN scenario generation model(ICGAN) comprehensively extracts wind power ti
extemporaneous
发表于 2025-3-28 19:34:15
Girls’ Embodied Experiences of Media Imagesg the spatial resolution and detail of meteorological variables. Leveraging the capabilities of diffusion models, specifically the SR3 and ResDiff architectures, we present a methodology for transforming low-resolution weather data into high-resolution outputs. Our experiments, conducted using the W
玩笑
发表于 2025-3-29 00:09:58
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发怨言
发表于 2025-3-29 03:11:55
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大炮
发表于 2025-3-29 09:37:40
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六边形
发表于 2025-3-29 11:40:38
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Mingle
发表于 2025-3-29 15:44:13
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保守
发表于 2025-3-29 21:53:54
Click-Through Rate Prediction Based on Filtering-Enhanced with Multi-head Attentionusers and items. Simultaneously, we introduce a multi-head attention (MHA) mechanism for the interaction selection, to capture features from different dimensions. Furthermore, the behavioral data reflecting user interests unavoidably contains noise, we attenuates noise by utilizing fast Fourier tran
使隔离
发表于 2025-3-30 00:20:11
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来自于
发表于 2025-3-30 07:10:06
LGCRS: LLM-Guided Representation-Enhancing for Conversational Recommender Systemrformance of the conversational recommender system. To tackle the aforementioned challenges, we propose a LLM-guided representation-enhancing method for conversational recommender system, which fuses collaborative signals and semantic information to improve recommendation performance and generate hi