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Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, Language models are unsupervised multitask learners, 2019."},{"key":"10.1016\/j.dsp.2026.105936_bib0081","unstructured":"G.Y. Kim, M.-h. Oh, ADAM optimization with adaptive batch selection, in: Y. Yue, A. Garg, N. Peng, F. Sha, R. Yu, (Eds.), 2025, pp. 44843\u201344884."},{"key":"10.1016\/j.dsp.2026.105936_bib0082","doi-asserted-by":"crossref","unstructured":"H. Yu, J. Huang, L. Li, M. Zhou, F. Zhao, Deep fractional fourier transform, (2023).","DOI":"10.52202\/075280-3181"},{"key":"10.1016\/j.dsp.2026.105936_bib0083","doi-asserted-by":"crossref","DOI":"10.1016\/j.dsp.2025.105317","article-title":"An accelerated unsupervised denoising model based on risk estimation and sparsity variational techniques","volume":"165","author":"Zhang","year":"2025","journal-title":"Digit. Signal. 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