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

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Challenges, Methods, Data–A?Survey of?Machine Learning in?Water Distribution Networksrease as a consequence of climate change. So far, the majority of approaches is based on hydraulics and engineering expertise. However, with the increasing availability of sensors, machine learning techniques constitute a promising tool. This work presents the main tasks in water distribution networ

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Enhancing Weather Predictions: Super-Resolution via?Deep Diffusion Modelsg 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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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/167622.jpg

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978-3-031-72355-1The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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Alasdair Vance,Jo Winther,Elham Shoorcheh of subjective factors on grading. Previous works tend to treat it solely as a regression or classification task, without considering the integration of both. Additionally, neural networks trained on limited samples often exhibit poor performance in capturing the deep semantics of texts. To enhance