Abstract: Over the past decades, various neural networks have been proposed with the rapid development of the machine learning field. In particular, graph neural networks using feature-vectors ...

F. Gama, A. G. Marques, G. Leus, and A. Ribeiro, "Convolutional Neural Network Architectures for Signals Supported on Graphs," IEEE Trans. Signal Process., vol. 67 ...

Transition metal complexes (TMCs) are of great scientific and practical interest for applications in catalysis, biological systems, photochemistry, and sustainability, with properties highly dependent ...

Abstract: The emerging graph neural network models (GNNs) have demonstrated great potential and success for downstream graph machine learning tasks, such as graph and node classification, link ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It ...

The best way to understand neural networks is to build one for yourself. Let's get started with creating and training a neural network in Java. Artificial neural networks are a form of deep learning ...

Efficient and accurate reconstruction and identification of tau lepton decays plays a crucial role in the program of measurements and searches under the study for the future high-energy particle ...