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Nakamura, W. Ye, M. Savvides, B. Raj, T. Shinozaki, B. Schiele, J. Wang, X. Xie, Y. Zhang, USB: A Unified Semi-supervised Learning Benchmark for Classification, in: Thirty-Sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2022, pp. 3938\u20133961."},{"key":"10.1016\/j.neucom.2024.128904_b99","first-page":"2825","article-title":"Scikit-learn: Machine learning in python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. 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