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Sponsored by eBay https://openreview.net/forum?id=X6kkUSFy3za&referrer=%5Bthe%20profile%20of%20Chunnan%20Wang%5D(%2Fprofile%3Fid%3D~Chunnan_Wang2) Auto-STGCN: Autonomous Spatial-Temporal Graph Convolutional Network Search | OpenReview graph convolutional networkautospatialtemporalsearch https://deepai.org/publication/gcod-graph-convolutional-network-acceleration-via-dedicated-algorithm-and-accelerator-co-design GCoD: Graph Convolutional Network Acceleration via Dedicated Algorithm and Accelerator Co-Design |... Dec 22, 2021 - 12/22/21 - Graph Convolutional Networks (GCNs) have emerged as the state-of-the-art graph learning model. However, it can be notoriously chal... graph convolutional network https://deepai.org/publication/filternet-a-neighborhood-relationship-enhanced-fully-convolutional-network-for-calf-muscle-compartment-segmentation FilterNet: A Neighborhood Relationship Enhanced Fully Convolutional Network for Calf Muscle... Jun 21, 2020 - 06/21/20 - Automated segmentation of individual calf muscle compartments from 3D magnetic resonance (MR) images is essential for developing q... convolutional networkfilternetneighborhoodrelationshipenhanced https://deepai.org/publication/fully-convolutional-network-with-multi-step-reinforcement-learning-for-image-processing Fully Convolutional Network with Multi-Step Reinforcement Learning for Image Processing | DeepAI Nov 10, 2018 - 11/10/18 - This paper tackles a new problem setting: reinforcement learning with pixel-wise rewards (pixelRL) for image processing. After the... convolutional networkmulti step https://arxiv.org/abs/2008.08407 [2008.08407] Instance-Aware Graph Convolutional Network for Multi-Label Classification Abstract page for arXiv paper 2008.08407: Instance-Aware Graph Convolutional Network for Multi-Label Classification graph convolutional networkmulti label2008instanceaware https://openreview.net/forum?id=7QHgFMT8kv&referrer=%5Bthe%20profile%20of%20Bin%20Luo%5D(%2Fprofile%3Fid%3D~Bin_Luo1) Label Guided Graph Optimized Convolutional Network for Semi-Supervised Learning | OpenReview Graph Convolutional Networks (GCNs) have been widely studied for semi-supervised learning tasks. It is known that the graph convolution operations in most of... semi supervised learningconvolutional networklabelguidedgraph https://deepai.org/publication/i-gcn-a-graph-convolutional-network-accelerator-with-runtime-locality-enhancement-through-islandization I-GCN: A Graph Convolutional Network Accelerator with Runtime Locality Enhancement through... Mar 7, 2022 - 03/07/22 - Graph Convolutional Networks (GCNs) have drawn tremendous attention in the past three years. Compared with other deep learning mod... graph convolutional network https://deepai.org/publication/spectral-based-graph-convolutional-network-for-directed-graphs Spectral-based Graph Convolutional Network for Directed Graphs | DeepAI Jul 21, 2019 - 07/21/19 - Graph convolutional networks(GCNs) have become the most popular approaches for graph data in these days because of their powerful ... graph convolutional networkdirected graphsspectralbaseddeepai https://aclanthology.org/2020.findings-emnlp.416/ Multi-hop Question Generation with Graph Convolutional Network - ACL Anthology Dan Su, Yan Xu, Wenliang Dai, Ziwei Ji, Tiezheng Yu, Pascale Fung. Findings of the Association for Computational Linguistics: EMNLP 2020. 2020. graph convolutional networkmultihopquestiongeneration https://openreview.net/forum?id=BQAN5ZMluTH&referrer=%5Bthe%20profile%20of%20Dawei%20Du%5D(%2Fprofile%3Fid%3D~Dawei_Du2) Scale Invariant Fully Convolutional Network: Detecting Hands Efficiently. | OpenReview Existing hand detection methods usually follow the pipeline of multiple stages with high computation cost, i.e., feature extraction, region proposal, bounding... scale invariantconvolutional networkfullydetectinghands https://deepai.org/publication/deep-iterative-residual-convolutional-network-for-single-image-super-resolution Deep Iterative Residual Convolutional Network for Single Image Super-Resolution | DeepAI Sep 7, 2020 - 09/07/20 - Deep convolutional neural networks (CNNs) have recently achieved great success for single image super-resolution (SISR) task due t... image super resolutionconvolutional networkdeepiterativeresidual https://openreview.net/forum?id=g1sESqlP214 SWNet: Surgical Workflow Recognition with Deep Convolutional Network | OpenReview Surgical workflow recognition has been playing an essential role in computer-assisted interventional systems for modern operating rooms. In this paper, we... convolutional networksurgicalworkflowrecognitiondeep https://www.easychair.org/publications/keyword/6Ctk Keyword: Graph Convolutional Network (GCN) graph convolutional networkkeywordgcn https://easychair.org/publications/preprint/W94X A Quantum Convolutional Network: a Comprehensive Review convolutional networkquantumcomprehensivereview https://openreview.net/forum?id=HJvvRoe0W An image representation based convolutional network for DNA classification | OpenReview A method to transform DNA sequences into 2D images using space-filling Hilbert Curves to enhance the strengths of CNNs convolutional networkimagerepresentationbaseddna https://deepai.org/publication/graph-revised-convolutional-network Graph-Revised Convolutional Network | DeepAI Nov 17, 2019 - 11/17/19 - Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveragin... convolutional networkgraphreviseddeepai https://openreview.net/forum?id=VeQBBm1MmTZ CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference |... Recently cloud-based graph convolutional network (GCN) has demonstrated great success and potential in many privacy-sensitive applications such as personal... graph convolutional networkfastscalableencryptedinference https://en.wikipedia.org/wiki/Convolutional_neural_network Convolutional neural network - Wikipedia convolutional neural networkwikipedia https://www.mdpi.com/1424-8220/20/21/6350 Radar Emitter Signal Recognition Based on One-Dimensional Convolutional Neural Network with... As the real electromagnetic environment grows complex and the quantity of radar signals turns massive, traditional methods, which require a large amount of... convolutional neural network