Robuta

https://openreview.net/forum?id=BJg73xHtvr&referrer=%5Bthe%20profile%20of%20Gary%20B%C3%A9cigneul%5D(%2Fprofile%3Fid%3D~Gary_B%C3%A9cigneul1) Constant Curvature Graph Convolutional Networks | OpenReview We generalize GCNs to (products of) spaces of constant sectional curvature using the gyrovector space formalism. graph convolutional networksconstant curvatureopenreview https://openreview.net/forum?id=QkRbdiiEjM AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models | OpenReview The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of... graph convolutional networksdeep modelsadaboostingopenreview https://openreview.net/forum?id=SFU-Gk27zX9&referrer=%5Bthe%20profile%20of%20Morteza%20Ramezani%5D(%2Fprofile%3Fid%3D~Morteza_Ramezani1) On the Importance of Sampling in Learning Graph Convolutional Networks | OpenReview Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of semi-supervised node classification tasks. Despite... graph convolutional networksimportancesampling https://openreview.net/forum?id=r-oRRT-ElX On Provable Benefits of Depth in Training Graph Convolutional Networks | OpenReview Reveal the discrepancy between the theoretical understanding of over-smoothing and the practical capabilities of GCNs. graph convolutional networksbenefits ofin trainingprovabledepth https://observablehq.com/@ameyasd/graph-convolutional-networks Graph Convolutional Networks / Ameya Daigavane | Observable Mar 3, 2022 - An interactive graph convolutional network! Given a graph with initial node features at each node , the network computes new node features! Choose weights and... graph convolutional networksameyaobservable https://openreview.net/forum?id=KeIuNChob1H Pseudo-Riemannian Graph Convolutional Networks | OpenReview A novel graph convolution network in pseudo-Riemannian manifold. graph convolutional networkspseudo riemannianopenreview https://velog.io/@sangwu99/UltraGCN-Ultra-Simplification-of-Graph-ConvolutionalNetworks-for-Recommendation-CIKM-2021 UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation (CIKM 2021) graph convolutional networksultrasimplification https://openreview.net/forum?id=JyKNuoZGux Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks | OpenReview Multiple sampling-based methods have been developed for approximating and accelerating node embedding aggregation in graph convolutional networks (GCNs)... graph convolutional networkscalibratedebiaslayerwise https://openreview.net/forum?id=HJxf53EtDr Unifying Graph Convolutional Networks as Matrix Factorization | OpenReview We unify graph convolutional networks as co-training and unitized matrix factorization. graph convolutional networksmatrix factorizationunifyingopenreview https://www.ornl.gov/publication/development-message-passing-based-graph-convolutional-networks-classifying-cancer Development of message passing-based graph convolutional networks for classifying cancer pathology... graph convolutional networksmessage passing https://deepai.org/publication/fully-linear-graph-convolutional-networks-for-semi-supervised-learning-and-clustering Fully Linear Graph Convolutional Networks for Semi-Supervised Learning and Clustering | DeepAI Nov 15, 2021 - 11/15/21 - This paper presents FLGC, a simple yet effective fully linear graph convolutional network for semi-supervised and unsupervised lea... graph convolutional networkssemi supervised learning https://openreview.net/forum?id=bDdfxLQITtu Not All Low-Pass Filters are Robust in Graph Convolutional Networks | OpenReview Graph Convolutional Networks (GCNs) are promising deep learning approaches in learning representations for graph-structured data. Despite the proliferation of... low pass filtersgraph convolutional networks https://aclanthology.org/2021.acl-long.344/ Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks - ACL Anthology Yuanhe Tian, Guimin Chen, Yan Song, Xiang Wan. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th... graph convolutional networksrelation extractiondependencydriven https://arxiv.org/abs/1910.07643 [1910.07643] Dynamic Graph Convolutional Networks Using the Tensor M-Product Abstract page for arXiv paper 1910.07643: Dynamic Graph Convolutional Networks Using the Tensor M-Product graph convolutional networks1910dynamic https://arxiv.org/abs/2006.04164 [2006.04164] Single-Layer Graph Convolutional Networks For Recommendation Abstract page for arXiv paper 2006.04164: Single-Layer Graph Convolutional Networks For Recommendation graph convolutional networkssingle layer2006recommendation https://www.mdpi.com/2072-4292/13/7/1404 Graph Convolutional Networks by Architecture Search for PolSAR Image Classification Classification of polarimetric synthetic aperture radar (PolSAR) images has achieved good results due to the excellent fitting ability of neural networks with... graph convolutional networksarchitecture searchimageclassification https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2021.624307/full Frontiers | CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease... Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development.... graph convolutional networks https://openreview.net/forum?id=A4igonCY9x&referrer=%5Bthe%20profile%20of%20Pallabi%20Ghosh%5D(%2Fprofile%3Fid%3D~Pallabi_Ghosh1) Stacked Spatio-Temporal Graph Convolutional Networks for Action Segmentation | OpenReview We propose novel Stacked Spatio-Temporal Graph Convolutional Networks (Stacked-STGCN) for action segmentation, i.e., predicting and localizing a sequence of... graph convolutional networksfor actionstackedspatiotemporal https://www.uni-augsburg.de/en/vkal/explaining-graph-convolutional-neural-networks-pat Explaining Graph Convolutional Neural Networks: Patient-Specific Subnetworks and Biomarker... convolutional neural networksexplaininggraphpatientspecific https://deepai.org/publication/multipath-graph-convolutional-neural-networks Multipath Graph Convolutional Neural Networks | DeepAI May 4, 2021 - 05/04/21 - Graph convolution networks have recently garnered a lot of attention for representation learning on non-Euclidean feature spaces. ... convolutional neural networksmultipathgraphdeepai https://deepai.org/publication/future-automation-engineering-using-structural-graph-convolutional-neural-networks Future Automation Engineering using Structural Graph Convolutional Neural Networks | DeepAI Aug 24, 2018 - 08/24/18 - The digitalization of automation engineering generates large quantities of engineering data that is interlinked in knowledge graph... convolutional neural networksfuture automationengineeringusingstructural