[PDF] Top 20 Attributed Graph Classification via Deep Graph Convolutional Neural Networks
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Attributed Graph Classification via Deep Graph Convolutional Neural Networks
... Graphs are a universal language for describing a set of complex systems (Zhang et al. 2018). There are complex systems all around us; society is a collection of over seven billion individuals, communication systems link ... See full document
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Rumor Classification Model Based on Deep Convolutional Neural Networks
... and classification is not high and the generalization is not strong, a rumor classification model based on deep convolutional neural networks is ...Using neural network to ... See full document
5
Cystoscopy Image Classification Using Deep Convolutional Neural Networks
... well-known convolutional neural networks (CNNs) and a multilayer neural network was applied to classify bladder cystoscopy ...of neural networks is determining the learning rate ... See full document
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Brain Tumor Classification Using Convolutional Neural Networks
... manual classification of tumor vs non-tumor in a particular ...automatic classification scheme are essential to prevent the death rate of ...tumor classification is very challenging task in large ... See full document
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Unified Framework For Deep Learning Based Text Classification
... Abstract: Deep learning has emerged as a very popular approach for solving large scale pattern recognition ...are deep learning based AI systems that have been trained to do sentiment analysis on social ... See full document
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Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks
... a deep convolution neural network, trained using transfer learning, capable of separating anatomically similar commercial and endangered woods of the Meliaceae family at both the genus and species level, ... See full document
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Geometric Hawkes Processes with Graph Convolutional Recurrent Neural Networks
... geometric deep learning becomes promising be- cause the convolutional framework can be applied on non- Euclidean data, ...formulate graph convolutional on spectral domain (Bruna et ... See full document
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Automatic Brain Tumor Segmentation by Deep Convolutional Networks and Graph Cuts
... the deep convolutional neural network architectures that were previously successful for semantic segmentation and medical image segmentation, such as fully convolutional neural ... See full document
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Aspect based Sentiment Classification with Aspect specific Graph Convolutional Networks
... recursive neural network using dependency trees, which achieved competitive results compared with strong ...adopting Graph convolutional networks (GCNs) (Kipf and Welling, 2017) ... See full document
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Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting
... Graph convolutional neural networks (GCNN) have become an increasingly active field of ...a graph with a pre-defined Lapla- cian matrix based on node ...our deep learning ... See full document
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Graph convolutional networks: a comprehensive review
... for graph-structured data, the underlying connectivity patterns are often complex and ...the graph properties can be ...the graph representation learning problem, many of them still suffer from their ... See full document
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A Novel Method for Remotely Sensed Hyperspectral Image Classification Based on Convolutional Neural Network
... image classification is the process of assigning land cover classes to ...and classification approaches affect the success of ...classification. Convolutional Neural Networks ... See full document
10
Bayesian Graph Convolutional Neural Networks for Semi-Supervised Classification
... Although there are many different flavours of GCNNs, all current versions process the graph as though it is a ground- truth depiction of the relationship between nodes. This is despite the fact that in many cases ... See full document
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Fine Grained Entity Typing via Hierarchical Multi Graph Convolutional Networks
... entity graph and apply graph-based algorithm on ...entity graph, jointly utilize entity fea- ture and graph structure to make type ...an attributed and predictive network embedding ... See full document
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Hypergraph Neural Networks
... hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph struc- ...network classification and visual object ... See full document
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Graph Convolutional Networks for Text Classification
... new graph neural network- based method for text ...large graph from an entire corpus, which contains words and documents as ...the graph with a Graph Convolutional Network (GCN) ... See full document
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Abusive Language Detection with Graph Convolutional Networks
... Supervised learning for abusive language detec- tion was first explored by Spertus (1997) who extracted rule-based features to train their classi- fier. Subsequently, manually-engineered lexical– syntactic features ... See full document
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Graph convolutional networks for exploring authorship hypotheses
... We also consider two structural features: first, indicator variables for the span’s level of gran- ularity (books, chapters, verses, or words), with the idea that sources differ in the processes that inserted them, e.g. ... See full document
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Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions
... We introduce a new method to tag Multiword Expressions (MWEs) using a linguistically in- terpretable language-independent deep learn- ing architecture. We specifically target dis- continuity, an under-explored ... See full document
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Sentiment Classification Via Recurrent Convolutional Neural Networks
... At present, there are some neural networks based methods that have been used in the sentiment classification task. Socher et al. [2, 3, 4] proposed the Recursive Neural Network (RecursiveNN). ... See full document
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