Results 41 to 50 of about 437,099 (249)
Cascade2vec: Learning Dynamic Cascade Representation by Recurrent Graph Neural Networks
IEEE Access, 2019 An information dissemination network (i.e., a cascade) with a dynamic graph structure is formed when a novel idea or message spreads from person to person.
Zhenhua Huang, Zhenyu Wang, Rui Zhang
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k-Nearest Neighbor Learning with Graph Neural Networks
Mathematics, 2021 k-nearest neighbor (kNN) is a widely used learning algorithm for supervised learning tasks. In practice, the main challenge when using kNN is its high sensitivity to its hyperparameter setting, including the number of nearest neighbors k, the distance ...
Seokho Kang
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FEBS Letters, EarlyView.We identified a systemic, progressive loss of protein S‐glutathionylation—detected by nonreducing western blotting—alongside dysregulation of glutathione‐cycle enzymes in both neuronal and peripheral tissues of Taiwanese SMA mice. These alterations were partially rescued by SMN antisense oligonucleotide therapy, revealing persistent redox imbalance as ...
Sofia Vrettou, Brunhilde Wirth
wiley +1 more source
Research on few-shot text classification techniques based on text-level-graph neural networks [PDF]
Journal of Hebei University of Science and TechnologyIn order to solve the problem of poor accuracy of text classification in text graph neural network with small samples, a text level graph neural network-prototypical (LGNN-Proto) was designed.
Xiangcheng AN, Baozhu LIU, Jingwei GAN
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Graph Neural Network for Traffic Flow Situation Prediction
Jisuanji kexue yu tansuo, 2021 Road network structure integrated traffic flow situation prediction is a highly nonlinear and complexly spatial-temporal dynamic correlation time-series data prediction problem. However, traditional traffic flow situation forecasting methods cannot model
JIANG Shan, DING Zhiming, XU Xinrun, YAN Jin
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On Sampling Strategies for Neural Network-based Collaborative Filtering
, 2017 Recent advances in neural networks have inspired people to design hybrid recommendation algorithms that can incorporate both (1) user-item interaction information and (2) content information including image, audio, and text.
Bordes Antoine +15 more
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IEEE Transactions on Pattern Analysis and Machine IntelligenceGeometric deep learning (GDL) models have demonstrated a great potential for the analysis of non-Euclidian data. They are developed to incorporate the geometric and topological information of non-Euclidian data into the end-to-end deep learning architectures. Motivated by the recent success of discrete Ricci curvature in graph neural network (GNNs), we
Cong Shen +3 more
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Molecular Oncology, EarlyView.Aldehyde dehydrogenase 1A1 (ALDH1A1) is a cancer stem cell marker in several malignancies. We established a novel epithelial cell line from rectal adenocarcinoma with unique overexpression of this enzyme. Genetic attenuation of ALDH1A1 led to increased invasive capacity and metastatic potential, the inhibition of proliferation activity, and ultimately ...
Martina Poturnajova +25 more
wiley +1 more source
Graphs, Convolutions, and Neural Networks: From Graph Filters to Graph Neural Networks
IEEE Signal Processing Magazine, 2020 Signal Processing ...
Gama, F. (author) +3 more
openaire +6 more sources
Dependency Parsing with Dilated Iterated Graph CNNs
, 2017 Dependency parses are an effective way to inject linguistic knowledge into many downstream tasks, and many practitioners wish to efficiently parse sentences at scale.
McCallum, Andrew, Strubell, Emma
core +1 more source