Results 31 to 40 of about 4,069,375 (260)

Anomaly detection with convolutional Graph Neural Networks

Journal of High Energy Physics, 2021
We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN autoencoders, we design a symmetric decoder capable of simultaneously ...
Oliver Atkinson, Akanksha Bhardwaj, Christoph Englert, Vishal S. Ngairangbam, Michael Spannowsky   +4 more
doaj   +1 more source

Casting a graph net to catch dark showers

SciPost Physics, 2021
Strongly interacting dark sectors predict novel LHC signatures such as semi-visible jets resulting from dark showers that contain both stable and unstable dark mesons.
Elias Bernreuther, Thorben Finke, Felix Kahlhoefer, Michael Krämer, Alexander Mück
doaj   +1 more source

Degree-Aware Graph Neural Network Quantization

Entropy, 2023
In this paper, we investigate the problem of graph neural network quantization. Despite the great success on convolutional neural networks, directly applying current network quantization approaches to graph neural networks faces two challenges.
Ziqin Fan, Xi Jin
doaj   +1 more source

Affinity-Point Graph Convolutional Network for 3D Point Cloud Analysis

Applied Sciences, 2022
Efficient learning of 3D shape representation from point cloud is one of the biggest requirements in 3D computer vision. In recent years, convolutional neural networks have achieved great success in 2D image representation learning.
Yang Wang, Shunping Xiao
doaj   +1 more source

Jumping Knowledge Based Spatial-Temporal Graph Convolutional Networks for Automatic Sleep Stage Classification [PDF]

, 2022
A novel jumping knowledge spatial-temporal graph convolutional network (JK-STGCN) is proposed in this paper to classify sleep stages. Based on this method, different types of multi-channel bio-signals, including electroencephalography (EEG ...
Ji, Xiaopeng, Wen, Peng, Li, Yan
core   +1 more source

Integrating convolutional neural networks into a sparse distributed representation model based on mammalian cortical learning

, 2016
Biological brains exhibit a remarkable capacity to recognise real-world patterns effectively. Despite major advances in neuroscience over the last few decades, an understanding of the brain's underlying mechanisms for pattern recognition remains ...
Daniel E. Padilla, McDonnell, M.D., Mark D. McDonnell, Padilla, D.E.   +3 more
core   +1 more source

Deep learning-based spatial-temporal graph neural networks for price movement classification in crude oil and precious metal markets

Machine Learning with Applications
In this study, we adapt three spatial-temporal graph neural network models to the unique characteristics of crude oil, gold, and silver markets for forecasting purposes.
Parisa Foroutan, Salim Lahmiri
doaj   +1 more source

Pseudo-Riemannian Graph Convolutional Networks

, 2022
Graph convolutional networks (GCNs) are powerful frameworks for learning embeddings of graph-structured data. GCNs are traditionally studied through the lens of Euclidean geometry.
Staab, S, Zhou, C, Potyka, N, Xiong, B, Zhu, S, Pan, S   +5 more
core  

Deep recurrent graph neural networks [PDF]

, 2020
Graph Neural Networks (GNN) show good results in classification and regression on graphs, notwithstanding most GNN models use a limited depth. In fact, they are composed of only a few stacked graph convolutional layers.
Pasa L., Sperduti A., Navarin N.
core  

A Review of Graph Neural Networks and Their Applications in Power Systems

Journal of Modern Power Systems and Clean Energy, 2022
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains.
Wenlong Liao, Birgitte Bak-Jensen, Jayakrishnan Radhakrishna Pillai, Yuelong Wang, Yusen Wang   +4 more
doaj   +1 more source