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

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Michael J. Larson,Mikle South,Tricia Merkleyd on graph neural networks (GNNs) is currently the mainstream technology, however, it also encounters challenges in terms of feature interactions and user interests. In the context of RSs, the individual attribute information associated with each entity holds significant importance beyond the inhere

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Elizabeth C. Winter,O. Joseph Bienvenuata sparsity and noise interference. Existing contrastive sequential recommendation models pull the embeddings of positive sequence pairs close, and train sequence encoders to be invariant to data augmentations, e.g., reordering, which could destroy information beneficial for the recommendation task

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Handbook of Children and Prejudicence (CEA). Our framework utilizes a Time-Series Model (TSM) for initial prediction followed by applying a Large Language Model (LLM) to refine the forecasts. We prompt the LLM to refine the TSM forecasts by demonstrating an example pair of past TSM predictions and their corresponding true future pri

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Kristine J. Ajrouch,Germine H. Awadrease 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