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In recent years, graph neural networks (GNNs) have been widely adopted in the representation learning of graph-structured data and provided state-of-the-art performance in various applications such as link prediction, node classification, and recommendation.
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Translating embeddings for modeling multi-relational data
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Discriminative unsupervised feature learning with convolutional neural networks
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Facial landmark detection by deep multi-task learning
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Personalized entity recommendation: A heterogeneous information network approach
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Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
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Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
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Deepak Pathak, Philipp Krahenbuhl, Jeff Donahue, Trevor Darrell, and Alexei A Efros · 2016
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Deepstereo: Learning to predict new views from the world’s imagery
John Flynn, Ivan Neulander, James Philbin, and Noah Snavely · 2016
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Meta-learning with memory-augmented neural networks
Adam Santoro, Sergey Bartunov, Matthew Botvinick, Daan Wierstra, and Timothy Lillicrap · 2016
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Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, and S Yu Philip · 2016
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Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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Rianne van den Berg, Thomas N Kipf, and Max Welling · 2017
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Multitask learning with low-level auxiliary tasks for encoder-decoder based speech recognition
Shubham Toshniwal, Hao Tang, Liang Lu, and Karen Livescu · 2017
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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A survey on multi-task learning
Yu Zhang and Qiang Yang · 2017
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Flood Sung, Yongxin Yang, Li Zhang, Tao Xiang, Philip H. S. Torr, and Timothy M. Hospedales · 2018
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One-shot relational learning for knowledge graphs
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Nsml: Meet the mlaas platform with a real-world case study
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Knowledge-aware graph neural networks with label smoothness regularization for recommender systems
Hongwei Wang, Fuzheng Zhang, Mengdi Zhang, Jure Leskovec, Miao Zhao, Wenjie Li, and Zhongyuan Wang · 2019
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BERT: pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Tinghui Zhou, Matthew Brown, Noah Snavely, and David G Lowe · 2017
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard Zemel · 2017
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Inductive representation learning on large graphs
William L. Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Nsml: A machine learning platform that enables you to focus on your models
Nako Sung, Minkyu Kim, Hyunwoo Jo, Youngil Yang, Jingwoong Kim, Leonard Lausen, Youngkwan Kim, Gayoung Lee, Donghyun Kwak, Jung-Woo Ha, et al · 2017
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Automatic differentiation in pytorch
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Pre-training graph neural networks for generic structural feature extraction
Ziniu Hu, Changjun Fan, Ting Chen, Kai-Wei Chang, and Yizhou Sun · 2019
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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Simplifying graph convolutional networks
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Graph transformer networks
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Learning what and where to transfer
Yunhun Jang, Hankook Lee, Sung Ju Hwang, and Jinwoo Shin · 2019
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Meta-gnn: On few-shot node classification in graph meta-learning
Fan Zhou, Chengtai Cao, Kunpeng Zhang, Goce Trajcevski, Ting Zhong, and Ji Geng · 2019
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Meta-graph: Few shot link prediction via meta learning
Avishek Joey Bose, Ankit Jain, Piero Molino, and William L Hamilton · 2019
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Meta relational learning for few-shot link prediction in knowledge graphs
Mingyang Chen, Wen Zhang, Wei Zhang, Qiang Chen, and Huajun Chen · 2019
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Learning to propagate for graph meta-learning
Lu Liu, Tianyi Zhou, Guodong Long, Jing Jiang, and Chengqi Zhang · 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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How to train your MAML
Antreas Antoniou, Harrison Edwards, and Amos J. Storkey · 2019
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Fast context adaptation via meta-learning
Luisa M. Zintgraf, Kyriacos Shiarlis, Vitaly Kurin, Katja Hofmann, and Shimon Whiteson · 2019
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Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
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A comprehensive survey on graph neural networks
Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S Yu Philip · 2020
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When does self-supervision help graph convolutional networks?
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2020
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Self-supervised learning on graphs: Deep insights and new direction
Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, and Jiliang Tang · 2020
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Strategies for pre-training graph neural networks
Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec · 2020
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Graph meta learning via local subgraphs
Kexin Huang and Marinka Zitnik · 2020
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