BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs ...

During the second part of the course, you’ll gain an in-depth understanding of a variety of machine learning techniques that you can apply when analysing big data including regression, variable ...

Methods: Patients from IMDC were identified and a ML classification and regression tree analysis was conducted, in which we grew a complex tree up to a depth of 30 with a minimum node split size of 2 ...

We have considered cost-sensitive graph classification in [2], and graph stream classification in [3][4][5]. Partial Dataset: We randomly select #Pos number of negative graphs from each original graph ...

The graph neural network (GNN) exhibits noteworthy performance in hyperspectral image classification (HSIC) due to its efficient message-passing structure, which employs the multilayer perceptron (MLP ...

In this paper, we investigate the ridge regression for multivariate labels by modelling each pixel and its surrounding pixels as a 3D tensor, and thereby propose a tensor ridge regression approach ...

After extracting the graph features, we developed four main classes of deep learning models for EEG classification. The first one is MLP ( Basha et al., 2020 ) which provides a simple and ...

Implementation of various neural graph classification model (not node classification) Training and test of various Graph Neural Networks (GNNs) models using graph classification datasets; Input graph: ...

We evaluate the performance of five classification methods and 10 regression methods to predict the location of defects. Our results demonstrate the efficacy of combining specific feature extraction ...

This strategy makes it difficult to retain potential relationships between papers, thereby limiting the performance of paper classification. To address the above issue, the contrastive graph attention ...