Titlebook: Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track; European Conference, Gianmarco De Francisci Mor

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書目名稱Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track影響因子(影響力)




書目名稱Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track影響因子(影響力)學(xué)科排名




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書目名稱Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track被引頻次




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Counterfactual Explanations for?Remote Sensing Time Series Data: An Application to?Land Cover Classi remote sensing, in which scientists and practitioners with diverse backgrounds work together to monitor the Earth’s surface. In this context, counterfactual explanations are an emerging tool to characterize the behaviour of machine learning systems, by providing a post-hoc analysis of a given class

胎兒

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Comprehensive Transformer-Based Model Architecture for?Real-World Storm Predictiontion model usually incurs excessive computational overhead due to employing atmosphere physical equations and complicated data assimilation. In this work, we strive to develop a lightweight and portable Transformer-based model architecture, which takes satellite and radar images as its input, for re

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Infelicity

Deep Spatiotemporal Clustering: A Temporal Clustering Approach for?Multi-dimensional Climate Dataate-of-the-art methods for unsupervised clustering use different similarity and distance functions but focus on either spatial or temporal features of the data. Concentrating on joint deep representation learning of spatial and temporal features, we propose Deep Spatiotemporal Clustering (DSC), a no

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Pre-training Contextual Location Embeddings in?Personal Trajectories via?Efficient Hierarchical Localing the location embedding is too expensive, due to the large number of locations to be trained in situations with fine-grained resolution or extensive target regions. Previous studies have handled less than ten thousand distinct locations, which is insufficient in the real-world applications. To t

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Leveraging Queue Length and?Attention Mechanisms for?Enhanced Traffic Signal Control Optimizationased TSC methods tend to focus primarily on the RL model structure while neglecting the significance of proper traffic state representation. Furthermore, some RL-based methods heavily rely on expert-designed traffic signal phase competition. In this paper, we present a novel approach to TSC that uti

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PICT: Precision-enhanced Road Intersection Recognition Using Cycling Trajectoriesever, the existing approaches mainly identify road intersections based on motor vehicles’ trajectories, and they fail to tackle unique challenges posed by cycling trajectories: (i) Cycling trajectories of minor intersections and their adjacent road segments are quite sparse. (ii) Turning behaviors o