Results 11 to 20 of about 4,069,375 (260)
Prototype-based Interpretable Graph Neural Networks [PDF]
, 2022 Graph neural networks have proved to be a key tool for dealing with many problems and domains such as chemistry, natural language processing and social networks.
Biagio La Rosa +2 more
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Graph neural networks for prediction of protein isoelectric points [PDF]
, 2022 Graph neural networks were used to model protein isoelectric points. Predictions contained markedly fewer outliers (predicted with errors > 0.5 pH units) compared to tools published in the literature, despite slightly higher root-mean-squared errors ...
Tom, Brenner
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Local Graph Convolutional Networks for Cross-Modal Hashing
, 2021 Cross-modal hashing aims to map the data of different modalities into a common binary space to accelerate the retrieval speed. Recently, deep cross-modal hashing methods have shown promising performance by applying deep neural networks to facilitate ...
Sen Wang +11 more
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Graph Convolutional Network for 3D Object Pose Estimation in a Point Cloud
Sensors, 2022 Graph Neural Networks (GNNs) are neural networks that learn the representation of nodes and associated edges that connect it to every other node while maintaining graph representation.
Tae-Won Jung +5 more
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Frontiers in Psychiatry, 2023 Depression, a prevalent global mental health disorder, necessitates precise treatment response prediction for the improvement of personalized care and patient prognosis. The Graph Convolutional Neural Networks (GCNs) have emerged as a promising technique
Zihe Lu +3 more
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Explainable Graph Neural Networks for Organic Cages [PDF]
, 2021 The development of accurate and explicable machine learning models to predict the properties of topologically complex systems is a challenge in material science.
Qi, Yuan +2 more
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Graph Neural Networks in Computer Vision - Architectures, Datasets and Common Approaches [PDF]
, 2023 Graph Neural Networks (GNNs) are a family of graph networks inspired by mechanisms existing between nodes on a graph. In recent years there has been an increased interest in GNN and their derivatives, i.e., Graph Attention Networks (GAT), Graph ...
Lukasikt, S, Krzywda, M, Gandomi, AH
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Evolutionary cellular configurations for designing feed-forward neural networks architectures [PDF]
, 2001 Proceeding of: 6th International Work-Conference on Artificial and Natural Neural Networks, IWANN 2001 Granada, Spain, June 13–15, 2001In the recent years, the interest to develop automatic methods to determine appropriate architectures of feed-forward ...
Gutiérrez Sánchez, Germán +6 more
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Graph Neural Network for Protein–Protein Interaction Prediction: A Comparative Study
Molecules, 2022 Proteins are the fundamental biological macromolecules which underline practically all biological activities. Protein–protein interactions (PPIs), as they are known, are how proteins interact with other proteins in their environment to perform biological
Hang Zhou +4 more
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Graph-Informed Neural Networks for Regressions on Graph-Structured Data
Mathematics, 2022 In this work, we extend the formulation of the spatial-based graph convolutional networks with a new architecture, called the graph-informed neural network (GINN).
Stefano Berrone +4 more
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