Fetching the paper…
Reading the bibliography…
In the evolving landscape of machine learning, the adaptation of pre-trained models through prompt tuning has become increasingly prominent.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 1901
Earlier work this paper cites.
ChEMBL: a large-scale bioactivity database for drug discovery
Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al · 2012
Earlier work this paper cites.
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
ZINC 15–ligand discovery for everyone
Teague Sterling and John J Irwin. 2015 · 2015
Earlier work this paper cites.
Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition . 770–778
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016 · 2016
Earlier work this paper cites.
Variational Graph Auto-Encoders
Thomas N Kipf and Max Welling. 2016 · 2016
Earlier work this paper cites.
Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations
Thomas N. Kipf and Max Welling. 2017 · 2017
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Earlier work this paper cites.
Large-scale comparison of machine learning methods for drug target prediction on ChEMBL
Andreas Mayr, Günter Klambauer, Thomas Unterthiner, Marvin Steijaert, Jörg K Wegner, Hugo Ceulemans, Djork-Arné Clevert, and Sepp Hochreiter. 2018 · 2018
Earlier work this paper cites.
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 Pande. 2018 · 2018
Earlier work this paper cites.
Representation learning on graphs with jumping knowledge networks. In International conference on machine learning . PMLR, 5453–5462
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018 · 2018
Earlier work this paper cites.
Hierarchical graph representation learning with differentiable pooling
Zhitao Ying, Jiaxuan You, Christopher Morris, Xiang Ren, Will Hamilton, and Jure Leskovec. 2018 · 2018
Earlier work this paper cites.
Self-attention graph pooling. In International conference on machine learning . PMLR, 3734–3743
Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019 · 2019
Earlier work this paper cites.
Graph convolutional networks with eigenpooling. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining . 723–731
Yao Ma, Suhang Wang, Charu C Aggarwal, and Jiliang Tang. 2019 · 2019
Earlier work this paper cites.
Revisiting graph neural networks: All we have is low-pass filters
Hoang Nt and Takanori Maehara. 2019 · 2019
Earlier work this paper cites.
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In International Conference on Learning Representations
Fan-Yun Sun, Jordan Hoffman, Vikas Verma, and Jian Tang. 2019 · 2019
Earlier work this paper cites.
Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning . PMLR, 6105–6114
Mingxing Tan and Quoc Le. 2019 · 2019
Earlier work this paper cites.
Deep Graph Infomax. In International Conference on Learning Representations
Petar Veličković, William Fedus, William L. Hamilton, Pietro Liò, Yoshua Bengio, and R Devon Hjelm. 2019 · 2019
Earlier work this paper cites.
Simplifying graph convolutional networks. In International conference on machine learning . PMLR, 6861–6871
Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019 · 2019
Earlier work this paper cites.
How Powerful are Graph Neural Networks?. In International Conference on Learning Representations
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019 · 2019
Earlier work this paper cites.
Evolution of resilience in protein interactomes across the tree of life
Marinka Zitnik, Rok Sosič, Marcus W Feldman, and Jure Leskovec. 2019 · 2019
Cited alongside, same era.
Spectral clustering with graph neural networks for graph pooling. In International conference on machine learning . PMLR, 874–883
Filippo Maria Bianchi, Daniele Grattarola, and Cesare Alippi. 2020 · 2020
Cited alongside, same era.
Memory-Based Graph Networks. In International Conference on Learning Representations
Amir Hosein Khasahmadi, Kaveh Hassani, Parsa Moradi, Leo Lee, and Quaid Morris. 2020 · 2020
Cited alongside, same era.
Towards deeper graph neural networks. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining . 338–348
Meng Liu, Hongyang Gao, and Shuiwang Ji. 2020 · 2020
Cited alongside, same era.
Path integral based convolution and pooling for graph neural networks
Zheng Ma, Junyu Xuan, Yu Guang Wang, Ming Li, and Pietro Liò. 2020 · 2020
Cited alongside, same era.
Exploring Visual Prompts for Adapting Large-Scale Models
Hyojin Bahng, Ali Jahanian, Swami Sankaranarayanan, and Phillip Isola. 2022 · 2022
Later among the works it cites.
A Survey of Vision-Language Pre-Trained Models. In Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI-22 , Lud De Raedt (Ed.). International Joint Conferences on Artificial Intelligence Organization, 5436–5443
Yifan Du, Zikang Liu, Junyi Li, and Wayne Xin Zhao. 2022 · 2022
Later among the works it cites.
Gppt: Graph pre-training and prompt tuning to generalize graph neural networks. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining . 1717–1727
Mingchen Sun, Kaixiong Zhou, Xin He, Ying Wang, and Xin Wang. 2022 · 2022
Later among the works it cites.
Simgrace: A simple framework for graph contrastive learning without data augmentation. In Proceedings of the ACM Web Conference 2022 . 1070–1079
Jun Xia, Lirong Wu, Jintao Chen, Bozhen Hu, and Stan Z Li. 2022a · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In International Conference on Learning Representations
Kenta Oono and Taiji Suzuki. 2020 · 2020
Cited alongside, same era.
Pre-trained models for natural language processing: A survey
Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, and Xuanjing Huang. 2020b · 2020
Cited alongside, same era.
Graph contrastive learning with augmentations
Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020 · 2020
Cited alongside, same era.
StructPool: Structured Graph Pooling via Conditional Random Fields. In International Conference on Learning Representations
Hao Yuan and Shuiwang Ji. 2020 · 2020
Cited alongside, same era.
Structure-feature based graph self-adaptive pooling. In Proceedings of The Web Conference 2020 . 3098–3104
Liang Zhang, Xudong Wang, Hongsheng Li, Guangming Zhu, Peiyi Shen, Ping Li, Xiaoyuan Lu, Syed Afaq Ali Shah, and Mohammed Bennamoun. 2020 · 2020
Cited alongside, same era.
Adaptive transfer learning on graph neural networks. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining . 565–574
Xueting Han, Zhenhuan Huang, Bang An, and Jing Bai. 2021 · 2021
Cited alongside, same era.
The Power of Scale for Parameter-Efficient Prompt Tuning. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing , Marie-Francine Moens, Xuanjing Huang, Lucia Specia, and Scott Wen-tau Yih (Eds.). Association for Computational Linguistics, Online and Punta Cana, Dominican Republic, 3045–3059
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Cited alongside, same era.
Towards effective and generalizable fine-tuning for pre-trained molecular graph models
Jun Xia, Jiangbin Zheng, Cheng Tan, Ge Wang, and Stan Z Li. 2022b · 2022
Later among the works it cites.
A survey of pretraining on graphs: Taxonomy, methods, and applications
Jun Xia, Yanqiao Zhu, Yuanqi Du, and Stan Z Li. 2022c · 2022
Later among the works it cites.
Bringing your own view: Graph contrastive learning without prefabricated data augmentations. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining . 1300–1309
Yuning You, Tianlong Chen, Zhangyang Wang, and Yang Shen. 2022 · 2022
Later among the works it cites.
Visual Prompting for Adversarial Robustness. In ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . 1–5
Aochuan Chen, Peter Lorenz, Yuguang Yao, Pin-Yu Chen, and Sijia Liu. 2023 · 2023
Later among the works it cites.
Universal Prompt Tuning for Graph Neural Networks. In Thirty-seventh Conference on Neural Information Processing Systems
Taoran Fang, Yunchao Mercer Zhang, Yang Yang, Chunping Wang, and Lei CHEN. 2023 · 2023
Later among the works it cites.
PRODIGY: Enabling In-context Learning Over Graphs. In Thirty-seventh Conference on Neural Information Processing Systems
Qian Huang, Hongyu Ren, Peng Chen, Gregor Kržmanc, Daniel Zeng, Percy Liang, and Jure Leskovec. 2023 · 2023
Later among the works it cites.
Exploring the benefits of visual prompting in differential privacy. In Proceedings of the IEEE/CVF International Conference on Computer Vision . 5158–5167
Yizhe Li, Yu-Lin Tsai, Chia-Mu Yu, Pin-Yu Chen, and Xuebin Ren. 2023 · 2023
Later among the works it cites.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2023c · 2023
Later among the works it cites.
GPT understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2023d · 2023
Later among the works it cites.
Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. In Proceedings of the ACM Web Conference 2023 . 417–428
Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang. 2023b · 2023
Later among the works it cites.
BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . 24224–24235
Changdae Oh, Hyeji Hwang, Hee-young Lee, YongTaek Lim, Geunyoung Jung, Jiyoung Jung, Hosik Choi, and Kyungwoo Song. 2023 · 2023
Later among the works it cites.
All in One: Multi-Task Prompting for Graph Neural Networks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (<conf-loc>, <city>Long Beach</city>, <state>CA</state>, <country>USA</country>, </conf-loc>) (KDD ’23) . Association for Computing Machinery, New York, NY, USA, 2120–2131
Xiangguo Sun, Hong Cheng, Jia Li, Bo Liu, and Jihong Guan. 2023 · 2023
Later among the works it cites.
Demystifying Oversmoothing in Attention-Based Graph Neural Networks. In Thirty-seventh Conference on Neural Information Processing Systems
Xinyi Wu, Amir Ajorlou, Zihui Wu, and Ali Jadbabaie. 2023 · 2023
Later among the works it cites.
Dual Modality Prompt Tuning for Vision-Language Pre-Trained Model
Yinghui Xing, Qirui Wu, De Cheng, Shizhou Zhang, Guoqiang Liang, Peng Wang, and Yanning Zhang. 2023 · 2023
Later among the works it cites.
AutoVP: An Automated Visual Prompting Framework and Benchmark. In The Twelfth International Conference on Learning Representations
Hsi-Ai Tsao, Lei Hsiung, Pin-Yu Chen, Sijia Liu, and Tsung-Yi Ho. 2024 · 2024
Closest in time.
Graph u-nets. In international conference on machine learning . PMLR, 2083–2092
Hongyang Gao and Shuiwang Ji. 2019 · 2092
Closest in time.