Results 91 to 100 of about 5,698,498 (295)

Robust Spatial Filtering With Graph Convolutional Neural Networks [PDF]

IEEE Journal of Selected Topics in Signal Processing, 2017
Convolutional Neural Networks (CNNs) have recently led to incredible breakthroughs on a variety of pattern recognition problems. Banks of finite impulse response filters are learned on a hierarchy of layers, each contributing more abstract information than the previous layer. The simplicity and elegance of the convolutional filtering process makes them
Felipe Petroski Such, Shagan Sah, Miguel Domínguez, Suhas Pillai, Chao Zhang, Andrew Michael, Nathan D. Cahill, Raymond W. Ptucha   +7 more
openaire   +2 more sources

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Intelligent recommendation system for College English courses based on graph convolutional networks

Heliyon
With the rapid development of international communication, the number of English courses has shown an explosive growth trend, which has caused a serious problem of information overload, resulting in poor teaching performance of recommended English ...
Chen Lilan, Jianqi Zhong
doaj   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Variational Graph Convolutional Neural Networks

CoRR
This work has been submitted to the IEEE for possible publication.
Illia Oleksiienko, Juho Kanniainen, Alexandros Iosifidis   +2 more
openaire   +2 more sources

Graph Classification with 2D Convolutional Neural Networks [PDF]

, 2019
Graph learning is currently dominated by graph kernels, which, while powerful, suffer some significant limitations. Convolutional Neural Networks (CNNs) offer a very appealing alternative, but processing graphs with CNNs is not trivial. To address this challenge, many sophisticated extensions of CNNs have recently been introduced.
Antoine J.-P. Tixier, Giannis Nikolentzos, Polykarpos Meladianos, Michalis Vazirgiannis   +3 more
openaire   +3 more sources

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Source Localization of Network Information Propagation via Invertible Graph Diffusion [PDF]

Jisuanji kexue yu tansuo
With the development of society, security issues in various types of networks have become increasingly prominent, especially network propagation issues.
ZHAI Wenshuo, ZHAO Xiang, CHEN Dong
doaj   +1 more source

Motif-based Convolutional Neural Network on Graphs

CoRR, 2017
This paper introduces a generalization of Convolutional Neural Networks (CNNs) to graphs with irregular linkage structures, especially heterogeneous graphs with typed nodes and schemas. We propose a novel spatial convolution operation to model the key properties of local connectivity and translation invariance, using high-order connection patterns or ...
Aravind Sankar, Xinyang Zhang 0002, Kevin Chen-Chuan Chang   +2 more
openaire   +3 more sources

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