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

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书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track影响因子(影响力)




书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track影响因子(影响力)学科排名




书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track网络公开度




书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track网络公开度学科排名




书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track被引频次




书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track被引频次学科排名




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书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track年度引用学科排名




书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track读者反馈




书目名称Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track读者反馈学科排名

dry-eye

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

胎儿

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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

construct

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

Throttle

curriculum

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

想象

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