Results 81 to 90 of about 37,604 (258)

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

Transactions of the Association for Computational Linguistics, 2019
We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation. To capture structural information associated with graphs, we investigate the problem of encoding graphs using graph ...
Guo, Zhijiang, Zhang, Yan, Teng, Zhiyang, Lu, Wei   +3 more
doaj   +1 more source

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

Path Connectivity Based Neighbor‑Awareness Node Classification Algorithm

Shuju Caiji Yu Chuli
Graph convolutional neural networks obtain the node representation by aggregating the neighbor node information with high similarity，and selecting the appropriate neighborhood for the node and conducting effective aggregation are the keys to the graph ...
ZHENG Wenping, WANG Xiaomin, HAN Zhaorong   +2 more
doaj   +1 more source

Adaptive Graph Convolutional Neural Networks

, 2018
Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure.
Zhu, Feiyun, Huang, Junzhou, Li, Ruoyu, Wang, Sheng   +3 more
core   +1 more source

SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks

, 2021
Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information.
Xu, M, Zhang, G, Guoqiang Zhang, Kenta Niwa, Min Xu, Niwa, Kenta, Xu, Min, Haimin Zhang, Zhang, Haimin, Zhang, Guoqiang, Zhang, H, Niwa, K   +11 more
core   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

Empowering Simple Graph Convolutional Networks [PDF]

Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, which tend to add complexity (and nonlinearity) to the model.
Pasa, L, Navarin, N, Sperduti, A, Erb, W
core   +1 more source

CMOS‐Integrated Synaptic Photoreceptor Chip Inspired by Insect Visual Processing

Advanced Science, EarlyView.
CMOS‐integrated Si QDs/ReS2 synaptic photoreceptor array mimics the parallel processing and wavelength‐selective strategy of insect vision. By combining intrinsic ultraviolet‐violet sensitivity with synaptic plasticity, the chip enables frontend sensory redundancy reduction without external filters, offering a scalable pathway toward lowpower ...
Jian Chai, Xinyi Xu, Yue Wang, Xiaochen Wang, Hailiang Wang, Yunfei Xie, Xinwei Zhang, Haoyu Wang, Hao Ning, Jiangming Lin, Yongliang Xie, Qihai Jiang, Baoshi Qiao, Xiaolei Ding, Luyao Ma, Shukai Duan, Zhenyi Ni, Huan Hu, Xin He, Fei Xue, Feichi Zhou, Lingfei Li, Srikrishna Chanakya Bodepudi, Rui Yuan, Bin Yu, Yang Xu   +25 more
wiley   +1 more source

Engineering Neuronal Network Connectivity Through Precise and Scalable Electrical Modulation

Advanced Science, EarlyView.
This study presents a scalable all‐electrical method for precise neuronal‐circuit reconfiguration based on high‐density microelectrode arrays. By employing biologically inspired plasticity rules, targeted connectivity changes were successfully induced and quantified across diverse neuronal preparations.
Sreedhar S. Kumar, Yannaël Bossard, Rachel Sava, Tobias Gänswein, Lorenca Sadiraj, Jean‐Samuel Dupré, Manuel Schröter, J. Gray Camp, Fernando Cardes, Julian Bartram, Andreas Hierlemann   +10 more
wiley   +1 more source

MGATs: Motif-Based Graph Attention Networks

Mathematics
In recent years, graph convolutional neural networks (GCNs) have become a popular research topic due to their outstanding performance in various complex network data mining tasks.
Jinfang Sheng, Yufeng Zhang, Bin Wang, Yaoxing Chang   +3 more
doaj   +1 more source