There are literally thousands of headphones available on the market — from affordable over-ear Bluetooth headphones to finely tuned in-ear monitors, and everything in between. If you've read our ...
Multi-modal Graph Convolution Network is a novel multi-modal recommendation framework based on graph convolutional networks, explicitly modeling modal-specific user preferences to enhance micro-video ...
A comprehensive PyTorch-based system for predicting cryptocurrency prices using a state-of-the-art Spatial-Temporal Graph Neural Network (ST-GNN) model. This advanced implementation integrates ...
Abstract: Hypercomplex graph convolutions with higher hypercomplex dimensions can extract more complex features in graphs and features with varying levels of complexity are suited for different ...
We report a fast and easy method (PACMAN) to assign partial atomic charges on metal–organic framework (MOF) and covalent–organic framework (COF) crystal structures based on graph convolution networks ...
Graph neural networks (GNNs) have been applied with great success across science and engineering, but we do not understand why they work so well. Motivated by experimental evidence of a rich phase ...
Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China ...
Abstract: Convolution on 3D point clouds is widely researched yet far from perfect in geometric deep learning. The traditional wisdom of convolution characterises feature correspondences ...
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