Results 11 to 20 of about 5,698,498 (295)
Graph Convolutional Neural Network [PDF]
Procedings of the British Machine Vision Conference 2016, 2016 The benefit of localized features within the regular domain has given rise to the use of Convolutional Neural Networks (CNNs) in machine learning, with great proficiency in the image classification.
Michael Edwards, Xianghua Xie
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Machine Learning and Knowledge Extraction, 2020 This paper explores the value of weak-ties in classifying academic literature with the use of graph convolutional neural networks. Our experiments look at the results of treating weak-ties as if they were strong-ties to determine if that assumption ...
Neda H. Bidoki +2 more
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Graph Neural Networks with Convolutional ARMA Filters [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Popular graph neural networks implement convolution operations on graphs based on polynomial spectral filters. In this paper, we propose a novel graph convolutional layer inspired by the auto-regressive moving average (ARMA) filter that, compared to polynomial ones, provides a more flexible frequency response, is more robust to noise, and better ...
Filippo Maria Bianchi +3 more
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A Network Scanning Organization Discovery Method Based on Graph Convolutional Neural Network
InformationWith the quick development of network technology, the number of active IoT devices is growing rapidly. Numerous network scanning organizations have emerged to scan and detect network assets around the clock.
Pengfei Xue +4 more
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康复学报ObjectiveTo evaluate the decoding accuracy and model performance of a graph spatio-temporal convolutional neural network (G-STCNN) in motor intention recognition of stroke patients.MethodsWe developed a novel G-STCNN model by integrating graph ...
XU Hui +5 more
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Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
IEEE Signal Processing Magazine, 2020 Network data can be conveniently modeled as a graph signal, where data values are assigned to nodes of a graph that describes the underlying network topology. Successful learning from network data is built upon methods that effectively exploit this graph structure.
Fernando Gama +3 more
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A Graph-Convolutional Neural Network for Addressing Small-Scale Reaction Prediction [PDF]
, 2021 We describe a graph-convolutional neural network (GCN) model whose reaction prediction capable as potent as the transformer model on sufficient data, and adopt the Baeyer-Villiger oxidation to explore their performance differences on limited data.
Yejian, Wu +3 more
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Graph-Time Convolutional Neural Networks
2021 IEEE Data Science and Learning Workshop (DSLW), 2021 Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author) +1 more
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Accurately Extending the Organic-Trained RexGen Graph Convolutional Neural Network to Inorganic Reaction Prediction [PDF]
, 2023 Computational chemists have taken great interest in machine learning in recent years, as techniques are being developed to produce faster predictions with higher accuracy. In 2019, Coley, et al, proposed a graph convolutional neural network (GCNN) model
Robert, Lavroff, Bobby, Judd
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Geometric Deep Learning for Protein–Protein Interaction Predictions
IEEE Access, 2022 This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux +3 more
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