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In recent years, prompt tuning has sparked a research surge in adapting pre-trained models.
Chembl: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J. Bellis, A. Patrícia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, and John P. Overington · 2012
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Zinc 15 – ligand discovery for everyone
T. Sterling and John J. Irwin · 2015
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Deep graph kernels
Pinar Yanardag and S. V. N. Vishwanathan · 2015
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec · 2017
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Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
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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 S. Pande · 2017
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Large-scale comparison of machine learning methods for drug target prediction on chembl† †electronic supplementary information (esi) available: Overview, data collection and clustering, methods, results, appendix. see doi: 10.1039/c8sc00148k
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin N. Steijaert, Jörg Kurt Wegner, Hugo Ceulemans, Djork-Arné Clevert, and Sepp Hochreiter · 2018
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Prioritizing network communities
Marinka Zitnik, Rok Sosi, and Jure Leskovec · 2018
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Diffusion improves graph learning
Johannes Klicpera, Stefan Weißenberger, and Stephan Günnemann · 2019
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Understanding attention and generalization in graph neural networks
Boris Knyazev, Graham W. Taylor, and Mohamed R. Amer · 2019
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Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
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Fan-Yun Sun, Jordan Hoffmann, Vikas Verma, and Jian Tang · 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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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Few-shot learning via learning the representation, provably
Simon Shaolei Du, Wei Hu, Sham M. Kakade, J. Lee, and Qi Lei · 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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Overcoming catastrophic forgetting in graph neural networks
Huihui Liu, Yiding Yang, and Xinchao Wang · 2020
Cited alongside, same era.
Rethinking pooling in graph neural networks
Diego Mesquita, Amauri H. de Souza, and Samuel Kaski · 2020
Cited alongside, same era.
Self-supervised graph transformer on large-scale molecular data
Yu Rong, Yatao Bian, Tingyang Xu, Weiyang Xie, Ying Wei, Wenbing Huang, and Junzhou Huang · 2020
Cited alongside, same era.
On the theory of transfer learning: The importance of task diversity
Nilesh Tripuraneni, Michael I. Jordan, and Chi Jin · 2020
Cited alongside, same era.
Understanding and improving information transfer in multi-task learning
Sen Wu, Hongyang Zhang, and Christopher Ré · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
Exploring visual prompts for adapting large-scale models
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola · 2022
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Equivariant subgraph aggregation networks
Beatrice Bevilacqua, Fabrizio Frasca, Derek Lim, Balasubramaniam Srinivasan, Chen Cai, G. Balamurugan, Michael M. Bronstein, and Haggai Maron · 2022
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Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
Ning Ding, Yujia Qin, Guang Yang, Fu Wei, Zonghan Yang, Yusheng Su, Shengding Hu, Yulin Chen, Chi-Min Chan, Weize Chen, Jing Yi, Weilin Zhao, Xiaozhi Wang, Zhiyuan Liu, Haitao Zheng, Jianfei Chen, Yang Liu, Jie Tang, Juan Li, and Maosong Sun · 2022
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Understanding and extending subgraph gnns by rethinking their symmetries
Fabrizio Frasca, Beatrice Bevilacqua, Michael Bronstein, and Haggai Maron · 2022
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Visual prompt tuning
Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen · 2020
Cited alongside, same era.
Reconstruction for powerful graph representations
Leonardo Cotta, Christopher Morris, and Bruno Ribeiro · 2021
Cited alongside, same era.
Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Doll’ar, and Ross B. Girshick · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Learning to pre-train graph neural networks
Yuanfu Lu, Xunqiang Jiang, Yuan Fang, and Chuan Shi · 2021
Cited alongside, same era.
Adversarial graph augmentation to improve graph contrastive learning
Susheel Suresh, Pan Li, Cong Hao, and Jennifer Neville · 2021
Cited alongside, same era.
Why do pretrained language models help in downstream tasks? an analysis of head and prompt tuning
Colin Wei, Sang Michael Xie, and Tengyu Ma · 2021
Cited alongside, same era.
Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
Closest in time.
Vision-and-language pretrained models: A survey
Siqu Long, Feiqi Cao, Soyeon Caren Han, and Haiqing Yang · 2022
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Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
Mingchen Sun, Kaixiong Zhou, Xingbo He, Ying Wang, and Xin Wang · 2022
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Unleashing the power of visual prompting at the pixel level
Junyang Wu, Xianhang Li, Chen Wei, Huiyu Wang, Alan Loddon Yuille, Yuyin Zhou, and Cihang Xie · 2022
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Simgrace: A simple framework for graph contrastive learning without data augmentation
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z. Li · 2022
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Class-aware visual prompt tuning for vision-language pre-trained model
Yinghui Xing, Qirui Wu, De Cheng, Shizhou Zhang, Guoqiang Liang, and Yanning Zhang · 2022
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Bringing your own view: Graph contrastive learning without prefabricated data augmentations
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen · 2022
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Few-shot learning on graphs
Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, N. Chawla, and Huan Liu · 2022
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From stars to subgraphs: Uplifting any gnn with local structure awareness
Lingxiao Zhao, Wei Jin, Leman Akoglu, and Neil Shah · 2022
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Graphprompt: Unifying pre-training and downstream tasks for graph neural networks
Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang · 2023
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Trainable projected gradient method for robust fine-tuning
Junjiao Tian, Xiaoliang Dai, Chih-Yao Ma, Zecheng He, Yen-Cheng Liu, and Zsolt Kira · 2023
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