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Recent works have impressively demonstrated that there exists a subnetwork in randomly initialized convolutional neural networks (CNNs) that can match the performance of the fully trained dense networks at initialization, without any optimization of the weights of the network (i.e., untrained networks).
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
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Estimating or propagating gradients through stochastic neurons for conditional computation
Yoshua Bengio, Nicholas Léonard, and Aaron Courville · 2013
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2016
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Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Predicting multicellular function through multi-layer tissue networks
Marinka Zitnik and Jure Leskovec · 2017
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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael Zhu and Suyog Gupta · 2017
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Deepinf: Social influence prediction with deep learning
Jiezhong Qiu, Jian Tang, Hao Ma, Yuxiao Dong, Kuansan Wang, and Jie Tang · 2018
Earlier work this paper cites.
Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
Earlier work this paper cites.
Deeper insights into graph convolutional networks for semi-supervised learning
Qimai Li, Zhichao Han, and Xiao-Ming Wu · 2018
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The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2018
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Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
Cited alongside, same era.
Scalable training of artificial neural networks with adaptive sparse connectivity inspired by network science
Decebal Constantin Mocanu, Elena Mocanu, Peter Stone, Phuong H Nguyen, Madeleine Gibescu, and Antonio Liotta · 2018
Cited alongside, same era.
Moleculenet: a benchmark for molecular machine learning
Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande · 2018
Cited alongside, same era.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Cited alongside, same era.
Attention based spatial-temporal graph convolutional networks for traffic flow forecasting
Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, and Huaiyu Wan · 2019
Cited alongside, same era.
Geometrically principled connections in graph neural networks
Shunwang Gong, Mehdi Bahri, Michael M Bronstein, and Stefanos Zafeiriou · 2020
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Understanding and resolving performance degradation in graph convolutional networks
Kuangqi Zhou, Yanfei Dong, Kaixin Wang, Wee Sun Lee, Bryan Hooi, Huan Xu, and Jiashi Feng · 2020
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Tackling over-smoothing for general graph convolutional networks
Wenbing Huang, Yu Rong, Tingyang Xu, Fuchun Sun, and Junzhou Huang · 2020
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Mitchell Wortsman, Vivek Ramanujan, Rosanne Liu, Aniruddha Kembhavi, Mohammad Rastegari, Jason Yosinski, and Ali Farhadi · 2020
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Rigging the lottery: Making all tickets winners
Utku Evci, Trevor Gale, Jacob Menick, Pablo Samuel Castro, and Erich Elsen · 2020
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Hattie Zhou, Janice Lan, Rosanne Liu, and Jason Yosinski · 2019
Cited alongside, same era.
The state of sparsity in deep neural networks
Trevor Gale, Erich Elsen, and Sara Hooker · 2019
Cited alongside, same era.
Simplifying graph convolutional networks
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger · 2019
Cited alongside, same era.
Pairnorm: Tackling oversmoothing in gnns
Lingxiao Zhao and Leman Akoglu · 2019
Cited alongside, same era.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2019
Cited alongside, same era.
Sparse networks from scratch: Faster training without losing performance
Tim Dettmers and Luke Zettlemoyer · 2019
Cited alongside, same era.
What’s hidden in a randomly weighted neural network?
Vivek Ramanujan, Mitchell Wortsman, Aniruddha Kembhavi, Ali Farhadi, and Mohammad Rastegari · 2020
Cited alongside, same era.
Later among the works it cites.
Soft threshold weight reparameterization for learnable sparsity
Aditya Kusupati, Vivek Ramanujan, Raghav Somani, Mitchell Wortsman, Prateek Jain, Sham Kakade, and Ali Farhadi · 2020
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Geom-gcn: Geometric graph convolutional networks
Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang · 2020
Later among the works it cites.
Pruning randomly initialized neural networks with iterative randomization
Daiki Chijiwa, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, and Tomohiro Inoue · 2021
Later among the works it cites.
Sparse training via boosting pruning plasticity with neuroregeneration
Shiwei Liu, Tianlong Chen, Xiaohan Chen, Zahra Atashgahi, Lu Yin, Huanyu Kou, Li Shen, Mykola Pechenizkiy, Zhangyang Wang, and Decebal Constantin Mocanu · 2021
Later among the works it cites.
Drawing robust scratch tickets: Subnetworks with inborn robustness are found within randomly initialized networks
Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, and Yingyan Lin · 2021
Later among the works it cites.
Graph posterior network: Bayesian predictive uncertainty for node classification
Maximilian Stadler, Bertrand Charpentier, Simon Geisler, Daniel Zügner, and Stephan Günnemann · 2021
Later among the works it cites.
Do transformers really perform bad for graph representation?
Chengxuan Ying, Tianle Cai, Shengjie Luo, Shuxin Zheng, Guolin Ke, Di He, Yanming Shen, and Tie-Yan Liu · 2021
Later among the works it cites.