Interestingly, this process is very similar to how convolutional neural networks extract features from pixel data. Accordingly, one very popular GNN architecture is the graph convolutional neural ...

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 ...

A Convolutional Neural Network (CNN ... Spatial Hierarchy Understanding The network's architecture enables comprehension of spatial relationships within images, allowing it to recognize objects ...

The learning capability of convolutional neural networks (CNNs ... Recent advances in light-weight deep learning models and network architecture search (NAS) algorithms are reviewed, starting with ...

Convolutional neural ... recurrent neural networks (RNNs). Conceptionally, RNNs do this by introducing feedback loops into the network’s architecture, enabling them to use information from ...

We applied three types of established GNN techniques, namely Crystal Graph Convolutional Neural Network (CGCNN), Materials Graph Network (MEGNET), and Atomistic Line Graph Neural Network (ALIGNN), to ...

We present a method for conditional time series forecasting based on an adaptation of the recent deep convolutional WaveNet architecture. The proposed network contains stacks of dilated convolutions ...