Spread the love“`html Understanding how to create a neural network can be a game-changer in the fields of artificial intelligence and machine learning. As industries increasingly rely on data-driven ...

Abstract: Graph convolutional neural networks (GCNNs) are becoming a model of choice for learning on irregular domains. However, due to the black-box nature of NNs, their underlying principles are ...

Abstract: Effectively capturing complex point cloud information is essential for advanced functionalities in consumer electronics, such as augmented reality, virtual simulations, and 3D printing.

Human pose forecasting is a complex structured-data sequence-modelling task, which has received increasing attention, also due to numerous potential applications. Research has mainly addressed the ...

Department of Chemistry and Research Institute for Natural Science, Korea University, Seoul 02841, Korea ...

The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of ...

Have you ever done a Google search to find a restaurant or look up what your favorite actor is up to? Most of us have, and therefore understand the benefit of knowledge graphs, possibly without even ...

Article Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to ...

A lot of combinatorial objects have a natural bialgebra structure. In this paper, we prove that the vector space spanned by labeled simple graphs is a bialgebra with the conjunction product and the ...

This repository is not maintained anymore. An updated version of the sparse codebase in this repo, together with many more GNN implementations, is available on https ...