Open AccessPosted Content
Graph Neural Networks: A Review of Methods and Applications
Jie Zhou,Ganqu Cui,Shengding Hu,Zhengyan Zhang,Cheng Yang,Zhiyuan Liu,Lifeng Wang,Changcheng Li,Maosong Sun +8 more
TLDR
A detailed review over existing graph neural network models is provided, systematically categorize the applications, and four open problems for future research are proposed.Abstract:
Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics systems, learning molecular fingerprints, predicting protein interface, and classifying diseases demand a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs. In recent years, variants of GNNs such as graph convolutional network (GCN), graph attention network (GAT), graph recurrent network (GRN) have demonstrated ground-breaking performances on many deep learning tasks. In this survey, we propose a general design pipeline for GNN models and discuss the variants of each component, systematically categorize the applications, and propose four open problems for future research.read more
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Journal ArticleDOI
Deep Learning for Generic Object Detection: A Survey
Li Liu,Li Liu,Wanli Ouyang,Xiaogang Wang,Paul Fieguth,Jie Chen,Xinwang Liu,Matti Pietikäinen +7 more
TL;DR: A comprehensive survey of the recent achievements in this field brought about by deep learning techniques, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics.
Journal ArticleDOI
A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
Moloud Abdar,Farhad Pourpanah,Sadiq Hussain,Dana Rezazadegan,Li Liu,Mohammad Ghavamzadeh,Paul Fieguth,Xiaochun Cao,Abbas Khosravi,U. Rajendra Acharya,U. Rajendra Acharya,U. Rajendra Acharya,Vladimir Makarenkov,Saeid Nahavandi +13 more
TL;DR: This study reviews recent advances in UQ methods used in deep learning and investigates the application of these methods in reinforcement learning (RL), and outlines a few important applications of UZ methods.
Journal ArticleDOI
Deep Learning on Graphs: A Survey
Ziwei Zhang,Peng Cui,Wenwu Zhu +2 more
TL;DR: Deep learning has been shown to be successful in a number of domains, ranging from acoustics, images, to natural language processing as discussed by the authors. However, applying deep learning to the ubiquitous graph data is non-trivial because of the unique characteristics of graphs.
Posted Content
Simplifying Graph Convolutional Networks
TL;DR: In this paper, the authors reduce the complexity of GCN by successively removing nonlinearities and collapsing weight matrices between consecutive layers, which corresponds to a fixed low-pass filter followed by a linear classifier.
Proceedings ArticleDOI
DeepGCNs: Can GCNs Go As Deep As CNNs?
TL;DR: In this article, a very deep GCN architecture is proposed to solve the vanishing gradient problem in point cloud semantic segmentation, which is based on graph convolutional networks (GCNs).
References
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Proceedings Article
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Proceedings Article
Attention is All you Need
Ashish Vaswani,Noam Shazeer,Niki Parmar,Jakob Uszkoreit,Llion Jones,Aidan N. Gomez,Lukasz Kaiser,Illia Polosukhin +7 more
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Journal ArticleDOI
Deep learning
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