The proposed kernel is applied to graph neural networks without edge-dependent filter generation ... @article{lei2020spherical, title={Spherical Kernel for Efficient Graph Convolution on 3D Point ...

Making Artificial Intelligence systems robustly perceive humans remains one of the most intricate challenges in computer ...

Institute of high energy physics, Chinese academy of sciences, Beijing 100049, China University of Chinese Academy of Sciences, Beijing 100049, China ...

Researchers at Carnegie Mellon University’s Robotics Institute have unveiled Hamba, a pioneering model designed to tackle one ...

and a siamese graph neural network to learn global-level interactions between two input graphs. Xiang Ling, Lingfei Wu, Saizhuo Wang, Tengfei Ma, Fangli Xu, Chunming Wu and Shouling Ji, Multilevel ...

We present STAG, a novel graph-based machine learning architecture for predicting TCR-pMHC binding specificity using 3D structure data. We show that STAG achieves comparable or better performance than ...

Abstract: Graph Neural Networks (GNNs) have been proven to be useful for learning graph-based knowledge. However, one of the drawbacks of GNN techniques is that they may get stuck in the problem of ...

Red. Loopable. Fly across a neuron's network, electric impulses passes by them. Loopable. Full HD. neural axon stock videos & royalty-free footage Biology nerve cell with biomedicine concept, 3d ...

Proteins play a crucial role in nearly all biological processes, yet predicting their complex interactions and designing ...