Results 41 to 50 of about 39,156 (266)

Generative Graph Convolutional Network for Growing Graphs [PDF]

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019
Modeling generative process of growing graphs has wide applications in social networks and recommendation systems, where cold start problem leads to new nodes isolated from existing graph. Despite the emerging literature in learning graph representation and graph generation, most of them can not handle isolated new nodes without nontrivial ...
Da Xu, Chuanwei Ruan, Kamiya Motwani, Evren Körpeoglu, Sushant Kumar, Kannan Achan   +5 more
openaire   +2 more sources

Movement Analysis for Neurological and Musculoskeletal Disorders Using Graph Convolutional Neural Network

Future Internet, 2021
Using optical motion capture and wearable sensors is a common way to analyze impaired movement in individuals with neurological and musculoskeletal disorders.
Ibsa K. Jalata, Thanh-Dat Truong, Jessica L. Allen, Han-Seok Seo, Khoa Luu   +4 more
doaj   +1 more source

Upright Adjustment With Graph Convolutional Networks

2020 IEEE International Conference on Image Processing (ICIP), 2020
We present a novel method for the upright adjustment of 360 images. Our network consists of two modules, which are a convolutional neural network (CNN) and a graph convolutional network (GCN). The input 360 images is processed with the CNN for visual feature extraction, and the extracted feature map is converted into a graph that finds a spherical ...
Raehyuk Jung, Sungmin Cho, Junseok Kwon
openaire   +2 more sources

Graph-Time Convolutional Neural Networks

2021 IEEE Data Science and Learning Workshop (DSLW), 2021
Spatiotemporal data can be represented as a process over a graph, which captures their spatial relationships either explicitly or implicitly. How to leverage such a structure for learning representations is one of the key challenges when working with graphs. In this paper, we represent the spatiotemporal relationships through product graphs and develop
Isufi, E. (author), Mazzola, Gabriele (author)   +1 more
openaire   +4 more sources

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022
Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring.
Kun Chen, Tianli Xie, Li Ma, Andrew E. Hudson, Qingsong Ai, Quan Liu   +5 more
doaj   +1 more source

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Sports behavior analysis technology based on GCN and domain knowledge graph

Discover Computing
To improve the performance of sports behavior recognition, the spatial temporal graph convolutional network is introduced to analyze the spatial temporal features of sports behavior, achieving accurate action recognition. In the experimental results, the
Jiaojiao Hu, Shengnan Ran
doaj   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

BMC Bioinformatics, 2023
Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour ...
Peng Chen, Haoran Zheng
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source