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Fine-tuning pre-trained models has recently yielded remarkable performance gains in graph neural networks (GNNs).
Language models are few-shot learners
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How powerful are graph neural networks?
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MolCPT: Molecule Continuous Prompt Tuning to Generalize Molecular Representation Learning
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Fast Graph Representation Learning with PyTorch Geometric
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Parameter-efficient transfer learning for NLP
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A comprehensive survey on graph neural networks
Wu, Z.; Pan, S.; Chen, F.; Long, G.; Zhang, C.; and Philip, S. Y. 2020 · 2020
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Graph contrastive learning with augmentations
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Pre-trained models: Past, present and future
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Delta tuning: A comprehensive study of parameter efficient methods for pre-trained language models
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Prompt Tuning for Graph Neural Networks
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Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning
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Advanced graph and sequence neural networks for molecular property prediction and drug discovery
Wang, Z.; Liu, M.; Luo, Y.; Xu, Z.; Xie, Y.; Wang, L.; Cai, L.; Qi, Q.; Yuan, Z.; Yang, T.; et al. 2022 · 2022
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Emergent abilities of large language models
Wei, J.; Tay, Y.; Bommasani, R.; Raffel, C.; Zoph, B.; Borgeaud, S.; Yogatama, D.; Bosma, M.; Zhou, D.; Metzler, D.; et al. 2022 · 2022
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Xia, J.; Wu, L.; Chen, J.; Hu, B.; and Li, S. Z. 2022a · 2022
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GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks
Liu, Z.; Yu, X.; Fang, Y.; and Zhang, X. 2023 · 2023
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A Survey of Graph Prompting Methods: Techniques, Applications, and Challenges
Wu, X.; Zhou, K.; Sun, M.; Wang, X.; and Liu, N. 2023 · 2023
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