
CayleyNets
 Referenced in 5 articles
[sw38090]
 CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters. The rise of graphstructured ... such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with ... paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs...

Pixel2Mesh
 Referenced in 5 articles
[sw31205]
 Limited by the nature of deep neural network, previous methods usually represent a 3D shape ... represents 3D mesh in a graphbased convolutional neural network and produces correct geometry...

RGCNN
 Referenced in 2 articles
[sw36662]
 images before feeding them into neural networks, which leads to voluminous data and quantization artifacts ... instead propose a regularized graph convolutional neural network (RGCNN) that directly consumes point clouds. Leveraging ... point cloud as signals on graph, and define the convolution over graph by Chebyshev polynomial...

PiNN
 Referenced in 2 articles
[sw30601]
 Networks of Molecules and Materials. Atomic neural networks (ANNs) constitute a class of machine learning ... PiNN, we implemented an interpretable graph convolutional neural network variant, PiNet, as well...

CGCNet
 Referenced in 1 article
[sw39633]
 Cell Graph Convolutional Network for Grading of Colorectal Cancer Histology Images. Colorectal cancer (CRC) grading ... present a novel cellgraph convolutional neural network (CGCNet) that converts each large histology ... introduce Adaptive GraphSage, which is a graph convolution technique that combines multilevel features...

Chainer Chemistry
 Referenced in 1 article
[sw39646]
 models (especially GCNN  Graph Convolutional Neural Network) for chemical property prediction...

Pose2Mesh
 Referenced in 1 article
[sw42752]
 Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human ... propose Pose2Mesh, a novel graph convolutional neural network (GraphCNN)based system that estimates...

HACTNet
 Referenced in 1 article
[sw39634]
 tissue distribution. Further, a hierarchical graph neural network (HACTNet) is proposed to efficiently ... proposed method outperformed recent convolutional neural network and graph neural network approaches for breast cancer...

SyncSpecCnn
 Referenced in 5 articles
[sw26163]
 with images that are 2D grids, shape graphs are irregular and nonisomorphic data structures ... vertex functions on them by convolutional neural networks, we resort to spectral CNN method that ... domain spanned by graph laplacian eigenbases. Under this setting, our network, named SyncSpecCNN, strive ... different graphs. Towards these goals, we introduce a spectral parameterization of dilated convolutional kernels...

DeepSphere
 Referenced in 2 articles
[sw29899]
 this repository implements a generalization of Convolutional Neural Networks (CNNs) to the sphere. We here ... model the discretised sphere as a graph of connected pixels. The resulting convolution is more ... data at multiple scales. The graph neural network model is based on ChebNet...

FeaStNet
 Referenced in 1 article
[sw40969]
 FeaStNet: FeatureSteered Graph Convolutions for 3D Shape Analysis. Convolutional neural networks (CNNs) have massively ... meshes or other graphstructured data, to which traditional local convolution operators do not directly...

CNTK
 Referenced in 9 articles
[sw21056]
 describes neural networks as a series of computational steps via a directed graph. In this ... directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix ... model types such as feedforward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs...

DeepGCNs
 Referenced in 2 articles
[sw38086]
 GCNs Go as Deep as CNNs? Convolutional Neural Networks (CNNs) achieve impressive performance ... Euclidean data. To overcome this challenge, Graph Convolutional Networks (GCNs) build graphs to represent...

LightGCN
 Referenced in 2 articles
[sw37571]
 Graph Convolution Network for Recommendation. Graph Convolution Network (GCN) has become new state ... originally designed for graph classification tasks and equipped with many neural network operations. However...

DeepAffinity
 Referenced in 1 article
[sw39160]
 learning model that unifies recurrent and convolutional neural networks has been proposed to exploit both ... alternative representations using protein sequences or compound graphs and a unified RNN/GCNNCNN model using graph...

PairNorm
 Referenced in 2 articles
[sw38087]
 Oversmoothing in GNNs. The performance of graph neural nets (GNNs) is known to gradually decrease ... partly attributed to oversmoothing, where repeated graph convolutions eventually make node embeddings indistinguishable. We take ... careful analysis of the graph convolution operator, which prevents all node embeddings from becoming ... easy to implement without any change to network architecture nor any additional parameters...

TFLearn
 Referenced in 1 article
[sw21054]
 prototyping through highly modular builtin neural network layers, regularizers, optimizers, metrics... Full transparency over ... TensorFlow graph, with support of multiple inputs, outputs and optimizers. Easy and beautiful graph visualization ... recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks...

DeepLGP
 Referenced in 0 articles
[sw37448]
 this study, we present a graph convolutional network (GCN) based method, named DeepLGP, for prioritizing ... applied to convolve a gene interaction network for encoding the features of genes and lncRNAs ... these features were used by the convolutional neural network for prioritizing target genes of lncRNAs...

GAP
 Referenced in 3221 articles
[sw00320]
 GAP is a system for computational discrete algebra...

Gmsh
 Referenced in 783 articles
[sw00366]
 Gmsh is a 3D finite element grid generator...