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Graph representation learning nowadays becomes fundamental in analyzing graph-structured data.
Self-Organization in a Perceptual Network
Ralph Linsker · 1988
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Self-Organizing Neural Network That Discovers Surfaces in Random-Dot Stereograms
Suzanna Becker and Geoffrey E. Hinton · 1992
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Collective Classification in Network Data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Gallagher, and Tina Eliassi-Rad · 2008
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Understanding the Difficulty of Training Deep Feedforward Neural Networks
Xavier Glorot and Yoshua Bengio · 2010
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Scikit-learn: Machine Learning in Python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake VanderPlas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot, and Edouard Duchesnay · 2011
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Noise-Contrastive Estimation of Unnormalized Statistical Models, with Applications to Natural Image Statistics
Michael Gutmann and Aapo Hyvärinen · 2012
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Distributed Representations of Words and Phrases and their Compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeffrey Dean · 2013
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DeepWalk: Online Learning of Social Representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Dropout: A Simple Way to Prevent Neural Networks From Overfitting
Nitish Srivastava, Geoffrey E. Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan R. Salakhutdinov · 2014
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GloVe: Global Vectors for Word Representation
Jeffrey Pennington, Richard Socher, and Christopher D. Manning · 2014
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FaceNet: A Unified Embedding for Face Recognition and Clustering
Florian Schroff, Dmitry Kalenichenko, and James Philbin · 2015
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2015
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node2vec: Scalable Feature Learning for Networks
Aditya Grover and Jure Leskovec · 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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Revisiting Semi-Supervised Learning with Graph Embeddings
Zhilin Yang, William W. Cohen, and Ruslan R. Salakhutdinov · 2016
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struc2vec: Learning Node Representations from Structural Identity
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo · 2017
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Semi-Supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
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Colorization as a Proxy Task for Visual Understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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Predicting Multicellular Function Through Multi-layer Tissue Networks
Marinka Zitnik and Jure Leskovec · 2017
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Network Embedding as Matrix Factorization: Unifying DeepWalk, LINE, PTE, and node2vec
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Contrastive Multiview Coding
Yonglong Tian, Dilip Krishnan, and Phillip Isola · 2019
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Learning Representations by Maximizing Mutual Information Across Views
Philip Bachman, R. Devon Hjelm, and William Buchwalter · 2019
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Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Mang Ye, Xu Zhang, Pong C. Yuen, and Shih-Fu Chang · 2019
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Deep Graph Infomax
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R. Devon Hjelm · 2019
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Learning Deep Representations by Mutual Information Estimation and Maximization
R. Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Philip Bachman, Adam Trischler, and Yoshua Bengio · 2019
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Data-Efficient Image Recognition with Contrastive Predictive Coding
Olivier J. Hénaff, Aravind Srinivas, Jeffrey De Fauw, Ali Razavi, Carl Doersch, S. M. Ali Eslami, and Aaron van den Oord · 2019
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Graph Attention Networks
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Unsupervised Feature Learning via Non-Parametric Instance Discrimination
Zhirong Wu, Yuanjun Xiong, Stella X. Yu, and Dahua Lin · 2018
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Unsupervised Representation Learning by Predicting Image Rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Representation Learning with Contrastive Predictive Coding
Aäron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking
Aleksandar Bojchevski and Stephan Günnemann · 2018
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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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On Variational Bounds of Mutual Information
Ben Poole, Sherjil Ozair, Aäron van den Oord, Alexander A. Alemi, and George Tucker · 2019
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Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Holanda de Souza Jr., Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
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Fast Graph Representation Learning with PyTorch Geometric
Matthias Fey and Jan Eric Lenssen · 2019
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Momentum Contrast for Unsupervised Visual Representation Learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 2020
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A Simple Framework for Contrastive Learning of Visual Representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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On Mutual Information Maximization for Representation Learning
Michael Tschannen, Josip Djolonga, Paul K. Rubenstein, Sylvain Gelly, and Mario Lucic · 2020
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DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang · 2020
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