Results 51 to 60 of about 142,397 (310)

Adaptive Graph Convolutional Neural Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 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. However, for most real data, the graph structures varies in both size and connectivity.
Ruoyu Li 0002, Sheng Wang 0001, Feiyun Zhu, Junzhou Huang   +3 more
openaire   +2 more sources

Semantic Graph Convolutional Networks for 3D Human Pose Regression

, 2020
In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node.
Kapadia, Mubbasir, Metaxas, Dimitris N., Peng, Xi, Tian, Yu, Zhao, Long   +4 more
core   +1 more source

Graph convolutional neural networks via scattering

Applied and Computational Harmonic Analysis, 2020
26 pages, 9 figures, 4 ...
Dongmian Zou, Gilad Lerman
openaire   +4 more sources

MIMO Graph Filters for Convolutional Neural Networks [PDF]

2018 IEEE 19th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), 2018
Superior performance and ease of implementation have fostered the adoption of Convolutional Neural Networks (CNNs) for a wide array of inference and reconstruction tasks. CNNs implement three basic blocks: convolution, pooling and pointwise nonlinearity.
Fernando Gama, Antonio G. Marques, Alejandro Ribeiro, Geert Leus   +3 more
openaire   +3 more sources

Fast Graph Convolutional Recurrent Neural Networks [PDF]

2019 53rd Asilomar Conference on Signals, Systems, and Computers, 2019
This paper proposes a Fast Graph Convolutional Neural Network (FGRNN) architecture to predict sequences with an underlying graph structure. The proposed architecture addresses the limitations of the standard recurrent neural network (RNN), namely, vanishing and exploding gradients, causing numerical instabilities during training.
Sai Kiran Kadambari, Sundeep Prabhakar Chepuri   +1 more
openaire   +2 more sources

Dual-channel deep graph convolutional neural networks

Frontiers in Artificial Intelligence
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhuoran Li, Zhuoran Li, Zhuoran Li, Zhuoran Li, Gege Li, Gege Li, Gege Li, Gege Li, Haixing Zhao, Haixing Zhao, Haixing Zhao, Haixing Zhao   +15 more
doaj   +1 more source

Scalable Graph Convolutional Networks With Fast Localized Spectral Filter for Directed Graphs

IEEE Access, 2020
Graph convolutional neural netwoks (GCNNs) have been emerged to handle graph-structured data in recent years. Most existing GCNNs are either spatial approaches working on neighborhood of each node, or spectral approaches based on graph Laplacian ...
Chensheng Li, Xiaowei Qin, Xiaodong Xu, Dujia Yang, Guo Wei   +4 more
doaj   +1 more source

Spectrum-based deep neural networks for fraud detection

, 2017
In this paper, we focus on fraud detection on a signed graph with only a small set of labeled training data. We propose a novel framework that combines deep neural networks and spectral graph analysis. In particular, we use the node projection (called as
Li, Jun, Lu, Aidong, Wu, Xintao, Yuan, Shuhan   +3 more
core   +1 more source

Graph Convolutional Networks for Text Classification

, 2018
Text classification is an important and classical problem in natural language processing. There have been a number of studies that applied convolutional neural networks (convolution on regular grid, e.g., sequence) to classification.
Luo, Yuan, Mao, Chengsheng, Yao, Liang
core   +1 more source

Texoskeletons: Developing the Fundamental Technologies for Creating Intelligent Soft Robotic Clothing With Integrated 1D Sensors and Actuators

Advanced Functional Materials, EarlyView.
ABSTRACT Traditional wearable exoskeletons rely on rigid structures, which limit comfort, flexibility, and everyday usability. This work introduces the fundamental technologies to create the first soft, lightweight, intelligent textile‐based exoskeletons (Texoskeletons) built using 1D sensors and actuators.
Amy Lukomiak, Rameesh Bulathsinghala, Jack Varga, Shuxin Meng, Kalana Marasinghe, Imaad Refai, Biyon Fernando, Amitkumar Patel, Chamika M. Halloluwa‐Arachchige, Carlos Oliveira, Arash M. Shahidi, Zahra Rahemtulla, Alexander Turner, Ziyun Ding, Bernard X. W. Liew, Andy Kerr, Ezio Preatoni, Theo Hughes‐Riley, Ishara Dharmasena, Pasindu Lugoda   +19 more
wiley   +1 more source