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Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging.
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) NAACL-HLT (1). Association for Computational Linguistics (2019)
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Kazi, A., Shekarforoush, S., Kortuem, K., Albarqouni, S., Navab, N., et al.: Self-attention equipped graph convolutions for disease prediction. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE (2019)
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Rong, Y., Bian, Y., Xu, T., Xie, W., Wei, Y., Huang, W., Huang, J.: Grover: Self-supervised message passing transformer on large-scale molecular data. Advances in Neural Information Processing Systems (2020)
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Cosmo, L., Kazi, A., Ahmadi, S.A., Navab, N., Bronstein, M.: Latent-graph learning for disease prediction. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer (2020)
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Hu, Z., Dong, Y., Wang, K., Chang, K.W., Sun, Y.: Gpt-gnn: Generative pre-training of graph neural networks. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2020)
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Mitani, A.A., Haneuse, S.: Small data challenges of studying rare diseases. JAMA network open 3
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Ouyang, C., Biffi, C., Chen, C., Kart, T., Qiu, H., Rueckert, D.: Self-supervision with superpixels: Training few-shot medical image segmentation without annotation. In: European Conference on Computer Vision. pp. 762–780. Springer (2020)
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2021
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Lu, Y., Jiang, X., Fang, Y., Shi, C.: Learning to pre-train graph neural networks. AAAI (2021)
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McDermott, M., Nestor, B., Kim, E., Zhang, W., Goldenberg, A., Szolovits, P., Ghassemi, M.: A comprehensive ehr timeseries pre-training benchmark. In: Proceedings of the Conference on Health, Inference, and Learning (2021)
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Ying, C., Cai, T., Luo, S., Zheng, S., Ke, G., He, D., Shen, Y., Liu, T.Y.: Do transformers really perform badly for graph representation? Advances in Neural Information Processing Systems 34
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