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检索条件"主题词=Machine Learning on Graphs"
21 条 记 录,以下是11-20 订阅
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Data Analytics on graphs Part II: Signals on graphs
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FOUNDATIONS AND TRENDS IN machine learning 2020年 第2-3期13卷 158-331页
作者: Stankovic, Ljubisa Mandic, Danilo Dakovic, Milos Brajovic, Milos Scalzo, Bruno Li, Shengxi Constantinides, Anthony G. Univ Montenegro Podgorica Montenegro Imperial Coll London London England
The area of Data Analytics on graphs deals with information processing of data acquired on irregular but structured graph domains. The focus of Part I of this monograph has been on both the fundamental and higher-orde... 详细信息
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Data-driven Predictive Latency for 5G: A Theoretical and Experimental Analysis Using Network Measurements  34
Data-driven Predictive Latency for 5G: A Theoretical and Exp...
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IEEE 34th Annual International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)
作者: Skocaj, Marco Conserva, Francesca Grande, Nicol Sarcone Orsi, Andrea Micheli, Davide Ghinamo, Giorgio Bizzarri, Simone Verdone, Roberto Univ Bologna DEI Bologna Italy WiLab CNIT Bologna Italy TIM Maglie Italy
The advent of novel 5G services and applications with binding latency requirements and guaranteed Quality of Service (QoS) hastened the need to incorporate autonomous and proactive decision-making in network managemen... 详细信息
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SELF-GROWING SPATIAL GRAPH NETWORK FOR CONTEXT-AWARE PEDESTRIAN TRAJECTORY PREDICTION
SELF-GROWING SPATIAL GRAPH NETWORK FOR CONTEXT-AWARE PEDESTR...
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IEEE International Conference on Image Processing (ICIP)
作者: Haddad, Sirin Lam, Siew-Kei Nanyang Technol Univ Sch Comp Sci & Engn Singapore Singapore
Pedestrian trajectory prediction is an active research area with recent works undertaken to embed accurate models of pedestrians social interactions and their contextual compliance into dynamic spatial graphs. However... 详细信息
来源: 评论
Simple Multi-resolution Gated GNN
Simple Multi-resolution Gated GNN
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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)
作者: Pasa, Luca Navarin, NicolO Sperduti, Alessandro Univ Padua Dept Math Padua Italy
Most Graph Neural Networks (GNNs) proposed in literature tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing a simple linear multi-resolution architect... 详细信息
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AutoNE: Hyperparameter Optimization for Massive Network Embedding  19
AutoNE: Hyperparameter Optimization for Massive Network Embe...
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25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD)
作者: Tu, Ke Ma, Jianxin Cui, Peng Pei, Jian Zhu, Wenwu Tsinghua Univ Beijing Peoples R China Simon Fraser Univ Burnaby BC Canada JD Com Beijing Peoples R China Beijing Natl Res Ctr Informat Sci & Technol BNRis Beijing Peoples R China
Network embedding (NE) aims to embed the nodes of a network into a vector space, and serves as the bridge between machine learning and network data. Despite their widespread success, NE algorithms typically contain a ... 详细信息
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An Untrained Neural Model for Fast and Accurate Graph Classification  32nd
An Untrained Neural Model for Fast and Accurate Graph Classi...
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32nd International Conference on Artificial Neural Networks (ICANN)
作者: Navarin, Nicolo Pasa, Luca Gallicchio, Claudio Sperduti, Alessandro Univ Padua Via Trieste 63 I-35121 Padua Italy Univ Pisa Largo Bruno Pontecorvo 3 I-56127 Pisa Italy Univ Trento DISI Trento Italy
Recent works have proven the feasibility of fast and accurate time series classification methods based on randomized convolutional kernels [5,32]. Concerning graph-structured data, the majority of randomized graph neu... 详细信息
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Backpropagation-free Graph Neural Networks  22
Backpropagation-free Graph Neural Networks
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22nd IEEE International Conference on Data Mining (ICDM)
作者: Pasa, Luca Navarin, Nicolo Erb, Wolfgang Sperduti, Alessandro Univ Padua Dept Math Padua Italy
We propose a class of neural models for graphs that do not rely on backpropagation for training, thus making learning more biologically plausible and amenable to parallel implementation in hardware. The base component... 详细信息
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Compact Graph Neural Network Models for Node Classification  22
Compact Graph Neural Network Models for Node Classification
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37th Annual ACM Symposium on Applied Computing
作者: Pasa, Luca Navarin, Nicolo Sperduti, Alessandro Univ Padua Dept Math Padua Italy
Recent research on graph convolutional networks tend to increase the complexity and non-linearity of graph convolution operators. Many of these operators result in models exploiting a huge number of learnable paramete... 详细信息
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Traffic speed prediction in the Lyon area using DCRNN  11
Traffic speed prediction in the Lyon area using DCRNN
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Modeles and Analyse des Reseaux: Approches Mathematiques and Informatiques - 11th Conference on Network Modeling and Analysis, MARAMI 2020
作者: Mensi, Fabio Furno, Angelo Cazabet, Remy ENS de Lyon CNRS IXXI Lyon France Univ. Lyon Univ. Gustave Eiffel LICIT UMR T9401 Lyon France Univ de Lyon CNRS Université Lyon 1 LIRIS UMR5205 Villeurbanne France
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Unsupervised framework for evaluating and explaining structural node embeddings of graphs
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JOURNAL OF COMPLEX NETWORKS 2024年 第2期12卷 cnae003-cnae003页
作者: Dehghan, Ashkan Siuta, Kinga Skorupka, Agata Betlen, Andrei Miller, David Kaminski, Bogumil Pralat, Pawel Toronto Metropolitan Univ Dept Math 350 Victoria St Toronto ON M5B 2K3 Canada SGH Warsaw Sch Econ Al Niepodleglosci 162 PL-02554 Warsaw Poland Patagona Technol Pickering ON Canada
An embedding is a mapping from a set of nodes of a network into a real vector space. Embeddings can have various aims like capturing the underlying graph topology and structure, node-to-node relationship, or other rel... 详细信息
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