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Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information.
Networks of spiking neurons: the third generation of neural network models
Wolfgang Maass · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Connected components in random graphs with given expected degree sequences
Fan Chung and Linyuan Lu · 2002
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Spiking neuron models: Single neurons, populations, plasticity
Wulfram Gerstner and Werner M Kistler · 2002
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Floating point operations in matrix-vector calculus
Raphael Hunger · 2005
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Simulation of networks of spiking neurons: a review of tools and strategies
Romain Brette, Michelle Rudolph, Ted Carnevale, et al · 2007
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Exact matrix completion via convex optimization
Emmanuel J. Candès and Benjamin Recht · 2012
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Σ \Sigma -optimality for active learning on gaussian random fields
Yifei Ma, Roman Garnett, and Jeff G. Schneider · 2013
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Predictive entropy search for efficient global optimization of black-box functions
José Miguel Hernández-Lobato, Matthew W. Hoffman, and Zoubin Ghahramani · 2014
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Vassilis Kalofolias, Xavier Bresson, Michael M. Bronstein, and Pierre Vandergheynst · 2014
Earlier work this paper cites.
A million spiking-neuron integrated circuit with a scalable communication network and interface
Paul A Merolla, John V Arthur, Rodrigo Alvarez-Icaza, et al · 2014
Earlier work this paper cites.
Spiking deep convolutional neural networks for energy-efficient object recognition
Yongqiang Cao, Yang Chen, and Deepak Khosla · 2015
Earlier work this paper cites.
Unsupervised learning of digit recognition using spike-timing-dependent plasticity
Peter U. Diehl and Matthew Cook · 2015
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Neuromorphic architectures for spiking deep neural networks
Giacomo Indiveri, Federico Corradi, and Ning Qiao · 2015
Earlier work this paper cites.
Collaborative filtering with graph information: Consistency and scalable methods
Nikhil Rao, Hsiang-Fu Yu, Pradeep Ravikumar, and Inderjit S. Dhillon · 2015
Earlier work this paper cites.
An analysis of deep neural network models for practical applications
Alfredo Canziani, Adam Paszke, and Eugenio Culurciello · 2016
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Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
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Convolutional networks for fast, energy-efficient neuromorphic computing
Steven K Esser, Paul A Merolla, John V Arthur, et al · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Training deep spiking neural networks using backpropagation
Junhaeng Lee, Tobi Delbrück, and Michael Pfeiffer · 2016
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Revisiting semi-supervised learning with graph embeddings
Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov · 2016
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Deep learning in spiking neural networks
Amirhossein Tavanaei, Masoud Ghodrati, Saeed Reza Kheradpisheh, Timothée Masquelier, and Anthony Maida · 2019
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Spikegrad: An ann-equivalent computation model for implementing backpropagation with spikes
Johannes Christian Thiele, Olivier Bichler, and Antoine Dupret · 2019
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Neural graph collaborative filtering
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua · 2019
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Heterogeneous graph attention network
Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Yanfang Ye, Peng Cui, and Philip S Yu · 2019
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Simplifying graph convolutional networks
Felix Wu, Amauri H Souza Jr, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q Weinberger · 2019
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Carbontracker: Tracking and predicting the carbon footprint of training deep learning models
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Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, and Michael M. Bronstein · 2017
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Geometric matrix completion with recurrent multi-graph neural networks
Federico Monti, Michael M. Bronstein, and Xavier Bresson · 2017
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Conversion of continuous-valued deep networks to efficient event-driven networks for image classification
Bodo Rueckauer, Iulia-Alexandra Lungu, Yuhuang Hu, Michael Pfeiffer, and Shih-Chii Liu · 2017
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Graph convolutional matrix completion
Rianne van den Berg, Thomas N. Kipf, and Max Welling · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, et al · 2017
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Fastgcn: fast learning with graph convolutional networks via importance sampling
Jie Chen, Tengfei Ma, and Cao Xiao · 2018
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Splinecnn: Fast geometric deep learning with continuous b-spline kernels
Matthias Fey, Jan Eric Lenssen, Frank Weichert, and Heinrich Müller · 2018
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Lasse F. Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan · 2020
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LISNN: improving spiking neural networks with lateral interactions for robust object recognition
Xiang Cheng, Yunzhe Hao, Jiaming Xu, and Bo Xu · 2020
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang · 2020
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Spiking-yolo: spiking neural network for energy-efficient object detection
Seijoon Kim, Seongsik Park, Byunggook Na, and Sungroh Yoon · 2020
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Towards deeper graph neural networks
Meng Liu, Hongyang Gao, and Shuiwang Ji · 2020
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Abstract interpretation based robustness certification for graph convolutional networks
Yang Liu, Jiaying Peng, Liang Chen, and Zibin Zheng · 2020
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AM-GCN: adaptive multi-channel graph convolutional networks
Xiao Wang, Meiqi Zhu, Deyu Bo, Peng Cui, Chuan Shi, and Jian Pei · 2020
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Graph neural network and multi-view learning based mobile application recommendation in heterogeneous graphs
Fenfang Xie, Zengxu Cao, Yangjun Xu, Liang Chen, and Zibin Zheng · 2020
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Spike-timing-dependent back propagation in deep spiking neural networks
Malu Zhang, Jiadong Wang, Zhixuan Zhang, et al · 2020
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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, and Maosong Sun · 2020
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Recent advances and new frontiers in spiking neural networks
Duzhen Zhang, Tielin Zhang, Shuncheng Jia, Qingyu Wang, and Bo Xu · 2022
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