Graph neural network
Graph Neural Network for Minimum Dominating Set
... the graph is either in D or adjacent to a vertex in D. In a graph on n nodes if there is a single node of degree n-1 then that single node forms a minimum dominating ...designed network called ...
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Text Level Graph Neural Network for Text Classification
... the graph neural network (GNN) techniques on text clas- sification, since GNN does well in handling complex structures and preserving global in- ...level graph structure which do not support ...
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A Lexicon Based Graph Neural Network for Chinese NER
... Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word infor- mation have achieved great ...lexicon-based graph neural ...
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Multi Channel Graph Neural Network for Entity Alignment
... Multi-channel Graph Neural Network model, MuGNN, which learns alignment-oriented KG embeddings for en- tity ...robust graph encoding ...
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Learning to Solve NP-Complete Problems: A Graph Neural Network for Decision TSP
... Graph Neural Networks (GNN) are a promising technique for bridging differential programming and combinatorial ...a network can be trained with sets of dual examples: given the optimal tour cost C ∗ , ...
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Subgraph Matching Using Graph Neural Network
... solving graph matching problems and have compared with greedy algorithm approach on graph matching ...sub- graph isomorphism problem in planar graphs in linear time, for any pattern of constant size ...
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Attributed Graph Classification via Deep Graph Convolutional Neural Networks
... learning, Graph Convolutional Neural Networks (GCNs) are a class of neural networks, explicitly designed for in-depth analysis of graph-structured data (Bruna et ...by graph signal ...
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AutoNet: Knowledge Graphs for Occasions Object Recognition
... Gated Graph Choose Search Neural Network(GG-CSNN) for learning Graph Neural ...the graph network as our initial set of active entities ...
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Representing Schema Structure with Graph Neural Networks for Text to SQL Parsing
... Research on parsing language to SQL has largely ignored the structure of the database (DB) schema, either because the DB was very simple, or because it was observed at both training and test time. In S PIDER , a ...
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Graph convolutional networks: a comprehensive review
... the graph convolutional models become ...two-layer graph convolution model often achieves the best performance in GCN [37] and GraphSAGE ...the graph convolutional models deeper, by borrowing the ...
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A Novel Neural Network Model for Joint POS Tagging and Graph based Dependency Parsing
... ditional graph- or transition-based parsing ap- proaches manually define a set of core and com- bined features associated with one-hot representa- tions (McDonald and Pereira, 2006; Nivre et ...to neural ...
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The Application of BP Neural Network in Leukocyte Classification Recognition
... Artificial neural network is the most widely used and most mature technology in artificial intelligence information fusion; classification recognition is one of the main ...BP neural network ...
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DEVELOPING A NEURO-GENETIC MODEL TO EFFECTIVELY PREDICT STOCK PRICES IN BSE SENSEX, 2018
... Stock market prediction is an important task for stock traders, applied researchers and stock investors. Various methods have been devised for the same. They are fundamental analysis, technical analysis, traditional time ...
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Speech Enhancement Using Neural Network
... simple network architecture with minimum ...(MLP) network and trained using the backpropagation algorithm (BPA). Neural network have existed for a long time and have recently enjoyed a ...
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Transition based Dependency Parsing Using Two Heterogeneous Gated Recursive Neural Networks
... Specifically, plain parser is as same as parser of Chen and Manning (2014). The difference between them is that plain parser only takes the nodes in stack and buffer into account, which uses a simpler feature template ...
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Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks
... convolutional neural net- work (FCNN) and conditional random fields (CRFs) as a post-processing step to segment brain tumours with an obtained DICE score of ...convolutional network (FCN) with ...
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Short Term Load Forecasting With Feed Forward Neural Network Algorithm
... Load forecasting plays an important role in power system planning and operation. Basic operating functions such as unit commitment, economic dispatch, fuel scheduling and unit maintenance, can be performed efficiently ...
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Intelligent monitoring of a complex, non-linear system using artificial neural networks
... List of Figures Chapter 2- Critical Review Fig 2.1: Graph of Neural Network PapersAnnounced 22 Fig 2.2: Graph of Genetic Algorithm PapersAnnounced 23 Fig 2.3: Windowing of Information 33[r] ...
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Graph based Dependency Parsing with Graph Neural Networks
... Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vin´ıcius Flores Zam- baldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, C ¸ aglar G¨ulc¸ehre, Francis ...
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Cholesky ANN models for predicting multivariate realized volatility
... Cholesky-Artificial Neural Networks specification here pre- sented provides a twofold advantage for this ...artificial neural networks allows to specify nonlin- ear relations without any particular ...
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