Titlebook: Artificial Neural Networks and Machine Learning – ICANN 2024; 33rd International C Michael Wand,Kristína Malinovská,Igor V. Tetko Conferenc

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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

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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

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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

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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