IEEE

From Cluster Assumption to Graph Convolution: Graph-Based Semi-Supervised Learning Revisited

Abstract: Graph-based semi-supervised learning (GSSL) has long been a research focus. Traditional methods are generally shallow learners, based on the cluster assumption. Recently, graph convolutional ...
GitHub

Hierarchical Convolution and Graph Net for Utilizing Structural Information of Image

Create a folder named 'ckpt' in './viz_nodes' and download the checkpoints of GraphConvNet-Ti or GraphConvNet-S and put them in './viz_nodes/ckpt' Open viz_demo.ipnb, and set arguments (arch, etc.) ...
Frontiers

Association prediction of lncRNAs and diseases using multiview graph convolution neural network

Long noncoding RNAs (lncRNAs) regulate physiological processes via interactions with macromolecules such as miRNAs, proteins, and genes, forming disease-associated regulatory networks. However, ...
IEEE

Evaluate the Convolution Integral and Convolution Sum Use Compact Formula

Abstract: Convolution integral and convolution summation play an important role in the analysis of the linear time invariant systems. At present, many text books have published in my home country or ...
Purdue University

Convolution integral: response to step

In a lecture example, we used the convolution integral approach to study the response of an undamped oscillator excited by the rectangular pulse shown below. Here we will apply the graphical ...