To overcome these challenges, we propose sigRGCN, a robust residual graph convolutional network for scRNA-seq data clustering. Specifically, we first construct a disturbed cell graph by injecting ...

This paper introduces a novel model, called Pre-Activation Residual Convolutional Network with Attention Modules (PRCN-AM), designed to enhance the accuracy and robustness of emotion recognition based ...

This repo contains an example implementation of the Simple Graph Convolution (SGC) model, described in the ICML2019 paper Simplifying Graph Convolutional Networks. SGC removes the nonlinearities and ...

This is a TensorFlow implementation of Graph Convolutional Networks for the task of (semi-supervised) classification of nodes in a graph, as described in our paper: Thomas N. Kipf, Max Welling, ...

It is significant to establish a precise dissolved oxygen (DO) model to obtain clear knowledge ablout the prospective changing conditions of the aquatic environment of marine ranches and to ensure the ...

By The Learning Network A new collection of graphs, maps and charts organized by topic and type from our “What’s Going On in This Graph?” feature. By The Learning Network Want to learn ...

These parameters were subsequently used as input to a model that integrates the Convolutional Block Attention Module and a Long Short-Term Memory neural network, designed to classify the severity ...

Practice: Make sure you can solve for maximum flow in a simple network using Ford-Fulkerson. Make up your own examples and solve them. Make sure you construct the correct residual network first.