They are developing a Quantum Convolutional Neural Network (QCNN) architecture to enhance the performance of traditional computer vision tasks using quantum mechanics principles. The Quantum ...

To address these issues, this study introduces a spatio-temporal graph convolutional network traffic crash prediction model based on Voronoi diagrams that considers geographical spatial distribution.

To this end, we propose a novel method called DEP-GCN (Drug Side Effects Prediction via Heterogeneous Multi-Relational Graph Convolutional Networks). Specifically, we design two protein auxiliary ...

The recent past has seen an increasing interest in Heterogeneous Graph Neural Networks (HGNNs), since many real-world graphs are heterogeneous in nature, from citation graphs to email graphs. However, ...

How to show more historical data? Use the zoom-out option. You can add up to 100 technical indicators to your graph, such as Linear Regression, CCI, ADX, and many more. In our commitment to ...

Convolutional Neural Networks (ConvNets or CNNs ... one of the biggest inventions of decade 2010s in deep learning community. A standard ConvNet architecture is typically made of 3 main layers that ...