Fetching the paper…
Reading the bibliography…
The rapid development of Internet technology has given rise to a vast amount of graph-structured data.
The MNIST database of handwritten digits
Yann LeCun. 1998 · 1998
Earlier work this paper cites.
Herding dynamical weights to learn. In Proceedings of the 26th Annual International Conference on Machine Learning . 1121–1128
Max Welling. 2009 · 2009
Earlier work this paper cites.
On the difficulty of training recurrent neural networks. In International conference on machine learning . Pmlr, 1310–1318
Razvan Pascanu, Tomas Mikolov, and Yoshua Bengio. 2013 · 2013
Earlier work this paper cites.
Kernel ridge regression
Vladimir Vovk. 2013 · 2013
Earlier work this paper cites.
Kernel ridge regression
Max Welling. 2013 · 2013
Earlier work this paper cites.
Divide and conquer kernel ridge regression. In Conference on learning theory . PMLR, 592–617
Yuchen Zhang, John Duchi, and Martin Wainwright. 2013 · 2013
Earlier work this paper cites.
Gradient-based hyperparameter optimization through reversible learning. In International conference on machine learning . PMLR, 2113–2122
Dougal Maclaurin, David Duvenaud, and Ryan Adams. 2015 · 2015
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning . PMLR, 40–48
Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016 · 2016
Earlier work this paper cites.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Earlier work this paper cites.
Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese. 2017 · 2017
Earlier work this paper cites.
Predict then propagate: Graph neural networks meet personalized pagerank
Johannes Gasteiger, Aleksandar Bojchevski, and Stephan Günnemann. 2018 · 2018
Earlier work this paper cites.
Neural tangent kernel: Convergence and generalization in neural networks
Arthur Jacot, Franck Gabriel, and Clément Hongler. 2018 · 2018
Earlier work this paper cites.
Modeling relational data with graph convolutional networks. In The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15 . Springer, 593–607
Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne Van Den Berg, Ivan Titov, and Max Welling. 2018 · 2018
Earlier work this paper cites.
Tongzhou Wang, Jun-Yan Zhu, Antonio Torralba, and Alexei A Efros. 2018 · 2018
Earlier work this paper cites.
On exact computation with an infinitely wide neural net
Sanjeev Arora, Simon S Du, Wei Hu, Zhiyuan Li, Russ R Salakhutdinov, and Ruosong Wang. 2019 · 2019
Earlier work this paper cites.
Graph neural tangent kernel: Fusing graph neural networks with graph kernels
Simon S Du, Kangcheng Hou, Russ R Salakhutdinov, Barnabas Poczos, Ruosong Wang, and Keyulu Xu. 2019 · 2019
Earlier work this paper cites.
Graph neural networks for social recommendation. In The world wide web conference . 417–426
Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, and Dawei Yin. 2019 · 2019
Cited alongside, same era.
Wei Hu, Zhiyuan Li, and Dingli Yu. 2019 · 2019
Cited alongside, same era.
Enhanced convolutional neural tangent kernels
Zhiyuan Li, Ruosong Wang, Dingli Yu, Simon S Du, Wei Hu, Ruslan Salakhutdinov, and Sanjeev Arora. 2019 · 2019
Cited alongside, same era.
Understanding and correcting pathologies in the training of learned optimizers. In International Conference on Machine Learning . PMLR, 4556–4565
Luke Metz, Niru Maheswaranathan, Jeremy Nixon, Daniel Freeman, and Jascha Sohl-Dickstein. 2019 · 2019
Cited alongside, same era.
Simplifying graph convolutional networks. In International conference on machine learning . PMLR, 6861–6871
Graph neural networks and their current applications in bioinformatics
Xiao-Meng Zhang, Li Liang, Lin Liu, and Ming-Jing Tang. 2021 · 2021
Later among the works it cites.
Dataset condensation with differentiable siamese augmentation. In International Conference on Machine Learning . PMLR, 12674–12685
Bo Zhao and Hakan Bilen. 2021 · 2021
Later among the works it cites.
Dataset distillation by matching training trajectories. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 4750–4759
George Cazenavette, Tongzhou Wang, Antonio Torralba, Alexei A Efros, and Jun-Yan Zhu. 2022 · 2022
Later among the works it cites.
Remember the past: Distilling datasets into addressable memories for neural networks
Zhiwei Deng and Olga Russakovsky. 2022 · 2022
Later among the works it cites.
Privacy for free: How does dataset condensation help privacy?. In International Conference on Machine Learning . PMLR, 5378–5396
Tian Dong, Bo Zhao, and Lingjuan Lyu. 2022 · 2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019 · 2019
Cited alongside, same era.
Graphsaint: Graph sampling based inductive learning method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019 · 2019
Cited alongside, same era.
Graph convolutional networks: a comprehensive review
Si Zhang, Hanghang Tong, Jiejun Xu, and Ross Maciejewski. 2019 · 2019
Cited alongside, same era.
Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world
Per Block, Marion Hoffman, Isabel J Raabe, Jennifer Beam Dowd, Charles Rahal, Ridhi Kashyap, and Melinda C Mills. 2020 · 2020
Cited alongside, same era.
Simple and deep graph convolutional networks. In International conference on machine learning . PMLR, 1725–1735
Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020 · 2020
Cited alongside, same era.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020 · 2020
Cited alongside, same era.
Graph neural network for robust public transit demand prediction
Can Li, Lei Bai, Wei Liu, Lina Yao, and S Travis Waller. 2020 · 2020
Cited alongside, same era.
Dataset meta-learning from kernel ridge-regression
Timothy Nguyen, Zhourong Chen, and Jaehoon Lee. 2020 · 2020
Cited alongside, same era.
Later among the works it cites.
A comprehensive survey on trustworthy recommender systems
Wenqi Fan, Xiangyu Zhao, Xiao Chen, Jingran Su, Jingtong Gao, Lin Wang, Qidong Liu, Yiqi Wang, Han Xu, Lei Chen, et al · 2022
Later among the works it cites.
Neural tangent kernel: A survey
Eugene Golikov, Eduard Pokonechnyy, and Vladimir Korviakov. 2022 · 2022
Later among the works it cites.
Condensing graphs via one-step gradient matching. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 720–730
Wei Jin, Xianfeng Tang, Haoming Jiang, Zheng Li, Danqing Zhang, Jiliang Tang, and Bing Yin. 2022 · 2022
Later among the works it cites.
Dataset condensation with contrastive signals. In International Conference on Machine Learning . PMLR, 12352–12364
Saehyung Lee, Sanghyuk Chun, Sangwon Jung, Sangdoo Yun, and Sungroh Yoon. 2022 · 2022
Later among the works it cites.
Graph condensation via receptive field distribution matching
Mengyang Liu, Shanchuan Li, Xinshi Chen, and Le Song. 2022 · 2022
Later among the works it cites.
What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness?
Nikolaos Tsilivis and Julia Kempe. 2022 · 2022
Later among the works it cites.
Cafe: Learning to condense dataset by aligning features. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 12196–12205
Kai Wang, Bo Zhao, Xiangyu Peng, Zheng Zhu, Shuo Yang, Shuo Wang, Guan Huang, Hakan Bilen, Xinchao Wang, and Yang You. 2022 · 2022
Later among the works it cites.
Improving social network embedding via new second-order continuous graph neural networks. In Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and data mining . 2515–2523
Yanfu Zhang, Shangqian Gao, Jian Pei, and Heng Huang. 2022 · 2022
Later among the works it cites.
Kernel Ridge Regression-Based Graph Dataset Distillation. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 2850–2861
Zhe Xu, Yuzhong Chen, Menghai Pan, Huiyuan Chen, Mahashweta Das, Hao Yang, and Hanghang Tong. 2023 · 2023
Closest in time.
Dataset condensation with distribution matching. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision . 6514–6523
Bo Zhao and Hakan Bilen. 2023 · 2023
Closest in time.
Structure-free Graph Condensation: From Large-scale Graphs to Condensed Graph-free Data
Xin Zheng, Miao Zhang, Chunyang Chen, Quoc Viet Hung Nguyen, Xingquan Zhu, and Shirui Pan. 2023 · 2023
Closest in time.
A graph neural network framework for social recommendations
Wenqi Fan, Yao Ma, Qing Li, Jianping Wang, Guoyong Cai, Jiliang Tang, and Dawei Yin. 2020 · 2047
Closest in time.