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

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ESSformer: Transformers with?ESS Attention for?Long-Term Series Forecasting for LTSF: ESSformer. It is built upon two essential components: (i) We adopt the Channel-Patch Independence architecture, where channels share the same model weights and have independent embeddings to avoid the impact of distribution shifts between channels. Patches are used to extract local semant

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https://doi.org/10.1007/978-3-031-72347-6artificial intelligence; classification; deep learning; generative models; graph neural networks; image p

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978-3-031-72346-9The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Mark R. Harrigan M.D.,John P. Deveikis M.D.consistencies in feature spaces, and constraints on downstream tasks. To address these issues, we propose an Adaptive Attention-based Cross-Modal Representation Integration Framework. This framework can adaptively capture and associate feature information from different modalities and effectively al

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Mark R. Harrigan M.D.,John P. Deveikis M.D.with multiple videos but are incorrectly labeled as exclusive to ones, leading to numerous incorrectly mismatched data. Furthermore, such ignorance may hinder model performance and flaw the evaluation of video retrieval. To alleviate this problem, we develop a training-free annotation pipeline, Boot

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