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ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring, , , , , , and . (2019)cite arxiv:1911.09785.BISTing Datapaths under Heterogeneous Test Schemes., , and . J. Electron. Test., 14 (1-2): 115-123 (1999)High Accuracy and High Fidelity Extraction of Neural Networks., , , , and . USENIX Security Symposium, page 1345-1362. USENIX Association, (2020)FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence., , , , , , , , and . NeurIPS, (2020)AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation., , , , and . ICLR, OpenReview.net, (2022)BISTing data paths at behavioral level., , and . ITC, page 672-680. IEEE Computer Society, (2000)Understanding and Improving Interpolation in Autoencoders via an Adversarial Regularizer., , , and . ICLR (Poster), OpenReview.net, (2019)Combinational techniques for sequential equivalence checking., , , and . FMCAD, page 145-149. IEEE, (2010)A Method for Trading off Test Time, Area and Fault Coverage in Datapath BIST Synthesis., , and . J. Electron. Test., 17 (3-4): 331-339 (2001)AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation., , , , and . CoRR, (2021)