Results 51 to 60 of about 229,000 (311)
Graph Learning in Robotics: A Survey
IEEE Access, 2023 Deep neural networks for graphs have emerged as a powerful tool for learning on complex non-euclidean data, which is becoming increasingly common for a variety of different applications. Yet, although their potential has been widely recognised in the machine learning community, graph learning is largely unexplored for downstream tasks such as robotics ...
Francesca Pistilli, Giuseppe Averta
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SCDVit: Semantic Change Detection Based on Sam-Vit and Semantic Consistency
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingIn recent years, change detection has been a hot research topic in remote sensing. Previous research has focused on binary change detection (BCD), limiting its practical applications.
Ming Chen, Wanshou Jiang
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Machines, 2022 In the crowd navigation, reinforcement learning based on graph neural network is a promising method, which effectively solves the poor navigation effect based on social interaction model and the freezing behavior of robot in extreme cases. However, since
Yazhou Lu, Xiaogang Ruan, Jing Huang
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Multiview Graph Learning With Consensus Graph
IEEE Transactions on Signal and Information Processing over NetworksGraph topology inference, i.e., learning graphs from a given set of nodal observations, is a significant task in many application domains. Existing approaches are mostly limited to learning a single graph assuming that the observed data is homogeneous.
Abdullah Karaaslanli, Selin Aviyente
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Learning graph Laplacian with MCP
Optimization Methods and Software, 2023 32 ...
Yangjing Zhang +2 more
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Reinforcement Learning on Graphs: A Survey
IEEE Transactions on Emerging Topics in Computational Intelligence, 2023 Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have been receiving great attention from the theoretical and algorithmic design communities in recent years, and there has been some pioneering work employing the research-rich Reinforcement Learning (RL) techniques ...
Mingshuo Nie +2 more
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Quantitative Stock Selection Model Using Graph Learning and a Spatial–Temporal Encoder
Journal of Theoretical and Applied Electronic Commerce ResearchIn the rapidly evolving domain of finance, quantitative stock selection strategies have gained prominence, driven by the pursuit of maximizing returns while mitigating risks through sophisticated data analysis and algorithmic models.
Tianyi Cao +4 more
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DeepWiN: Deep Graph Reinforcement Learning for User-Centric Radio Access Networks Automation [PDF]
, 2023 The future cellular networks are expected to support an increasing number of users with heterogeneous applications, requiring varying network resources. Therefore, the 6G and beyond cellular networks need to be elastic, and user-centric.
Shaukat, Maria
core
OpenWGL: open-world graph learning for unseen class node classification [PDF]
, 2021 Graph learning, such as node classification, is typically carried out in a closed-world setting. A number of nodes are labeled, and the learning goal is to correctly classify remaining (unlabeled) nodes into classes, represented by the labeled nodes.
Wu, M +5 more
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Design and analysis strategies for robust microbiome ageing research
FEBS Letters, EarlyView.The gut microbiome changes with age and associates with age‐related morbidity and mortality, establishing it as a potential biomarker and intervention target for ageing. Realising this potential requires methodological rigour, yet distinguishing biological signals from methodological artefacts remains challenging across cohorts. This review provides an
Mark Olenik +5 more
wiley +1 more source