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(2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929,."},{"issue":"5","key":"10.1016\/j.ipm.2026.104731_bib0013","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1049\/cmu2.12284","article-title":"A sequential roadmap to industry 6.0: Exploring future manufacturing trends","volume":"16","author":"Duggal","year":"2022","journal-title":"IET Communications"},{"key":"10.1016\/j.ipm.2026.104731_bib0014","series-title":"2022\u202fIEEE 9th international conference on data science and advanced analytics (DSAA)","first-page":"1","article-title":"Active lifelong anomaly detection with experience replay","author":"Faber","year":"2022"},{"issue":"10","key":"10.1016\/j.ipm.2026.104731_bib0015","doi-asserted-by":"crossref","first-page":"8137","DOI":"10.1007\/s10994-024-06524-z","article-title":"From MNIST to imagenet and back: Benchmarking continual curriculum learning","volume":"113","author":"Faber","year":"2024","journal-title":"Machine Learning"},{"key":"10.1016\/j.ipm.2026.104731_bib0016","article-title":"Adaptive memory replay for network intrusion detection: Tackling data drift and catastrophic forgetting","volume":"272","author":"FathimaAH","year":"2025","journal-title":"Computer Networks"},{"issue":"2","key":"10.1016\/j.ipm.2026.104731_bib0017","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1137\/090771806","article-title":"Finding structure with randomness: Probabilistic algorithms for constructing approximate matrix decompositions","volume":"53","author":"Halko","year":"2011","journal-title":"SIAM Review"},{"key":"10.1016\/j.ipm.2026.104731_bib0018","unstructured":"Hammarling, J. 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