Results 31 to 40 of about 228,260 (299)
Temporal network embedding framework with causal anonymous walks representations [PDF]
PeerJ Computer Science, 2022 Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding.
Ilya Makarov +7 more
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On Learning and Learned Data Representation by Capsule Networks [PDF]
IEEE Access, 2019 In this work, we investigate the following: 1) how the routing affects the CapsNet model fitting; 2) how the representation using capsules helps discover global structures in data distribution, and; 3) how the learned data representation adapts and generalizes to new tasks.
Ancheng Lin, Jun Li 0010, Zhenyuan Ma
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Extending a network-of-elaborations representation to polyphonic music: Schenker and species counterpoint. [PDF]
, 2004 A system of representing melodies as a network of elaborations has been developed, and used as the basis for software which generates melodies in response to the movements of a dancer.
Marsden, Alan, Alan Marsden
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Feature Hashing for Network Representation Learning [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 The goal of network representation learning is to embed nodes so as to encode the proximity structures of a graph into a continuous low-dimensional feature space. In this paper, we propose a novel algorithm called node2hash based on feature hashing for generating node embeddings. This approach follows the encoder-decoder framework.
Qixiang Wang +3 more
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Mathematics, 2021 Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network ...
Xin Xu +5 more
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Time representation in reinforcement learning models of the basal ganglia [PDF]
, 2013 Reinforcement learning (RL) models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection.
Sebastian eDupraz +29 more
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AttrHIN: Network Representation Learning Method for Heterogeneous Information Network
IEEE Access, 2021 Network representation learning can map complex network to the low dimensional vector space, capture the topological properties of networks, and reduce the time complexity and space complexity of the algorithm.
Qingbiao Zhou, Chen Wang, Qi Li
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Generative Adversarial Network and Meta-path Based Heterogeneous Network Representation Learning [PDF]
Jisuanji kexue, 2022 Most of the information works in real world are heterogeneous information networks (HIN).Network representation methods aiming to represent node data in low dimensional space have been widely used to analyze heterogeneous information networks,so as to ...
JIANG Zong-li, FAN Ke, ZHANG Jin-li
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Low-Bit Quantization for Attributed Network Representation Learning [PDF]
, 2019 Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data ...
Yang, Hong +14 more
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Node Classification Algorithm Based on Information Propagation Node Set for CTDN [PDF]
Jisuanji gongcheng, 2021 The study described in this paper addresses the problem of node classification in Continuous-Time Dynamic Network(CTDN).In this work, an information propagation node set is defined according to the features of the actual network information propagation ...
HUANG Xin, LI Yun, XIONG Jinyu
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