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Graph Neural Network (GNN) has been demonstrated its effectiveness in dealing with non-Euclidean structural data.
Laurens Van der Maaten and Geoffrey Hinton · 2008
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Empirical evaluation of gated recurrent neural networks on sequence modeling
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel · 2015
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2016
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Gaussian error linear units (gelus)
Dan Hendrycks and Kevin Gimpel · 2016
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Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton · 2016
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Scene graph generation by iterative message passing
Danfei Xu, Yuke Zhu, Christopher B Choy, and Li Fei-Fei · 2017
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Diffusion convolutional recurrent neural network: Data-driven traffic forecasting
Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu · 2017
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Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
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Inductive representation learning on large graphs
William L Hamilton, Rex Ying, and Jure Leskovec · 2017
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Graph r-cnn for scene graph generation
Jianwei Yang, Jiasen Lu, Stefan Lee, Dhruv Batra, and Devi Parikh · 2018
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Gaan: Gated attention networks for learning on large and spatiotemporal graphs
Jiani Zhang, Xingjian Shi, Junyuan Xie, Hao Ma, Irwin King, and Dit-Yan Yeung · 2018
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Simplifying graph convolutional networks, 2019
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr. au2, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Design space for graph neural networks
Jiaxuan You, Zhitao Ying, and Jure Leskovec · 2020
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An image is worth 16x16 words: Transformers for image recognition at scale, 2020
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2020
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Bootstrap your own latent: A new approach to self-supervised learning, 2020
Jean-Bastien Grill, Florian Strub, Florent Altché, Corentin Tallec, Pierre H. Richemond, Elena Buchatskaya, Carl Doersch, Bernardo Avila Pires, Zhaohan Daniel Guo, Mohammad Gheshlaghi Azar, Bilal Piot, Koray Kavukcuoglu, Rémi Munos, and Michal Valko · 2020
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Supervised contrastive learning for pre-trained language model fine-tuning
Beliz Gunel, Jingfei Du, Alexis Conneau, and Ves Stoyanov · 2020
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Self-supervised learning on graphs: Deep insights and new direction
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Hypergraph neural networks
Yifan Feng, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao · 2019
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Hyperbolic graph convolutional neural networks
Ines Chami, Rex Ying, Christopher Ré, and Jure Leskovec · 2019
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Hyperbolic graph neural networks, 2019
Qi Liu, Maximilian Nickel, and Douwe Kiela · 2019
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Lanczosnet: Multi-scale deep graph convolutional networks, 2019
Renjie Liao, Zhizhen Zhao, Raquel Urtasun, and Richard S. Zemel · 2019
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Deep graph infomax
Petar Velickovic, William Fedus, William L Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm · 2019
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Lightgcn: Simplifying and powering graph convolution network for recommendation
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton
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Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang · 2020
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Mlp-mixer: An all-mlp architecture for vision, 2021
Ilya Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, Mario Lucic, and Alexey Dosovitskiy · 2021
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Repmlp: Re-parameterizing convolutions into fully-connected layers for image recognition, 2021
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Supervised contrastive learning, 2021
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