{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,15]],"date-time":"2025-04-15T05:39:17Z","timestamp":1744695557689,"version":"3.40.3"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030954697"},{"type":"electronic","value":"9783030954703"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-030-95470-3_26","type":"book-chapter","created":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:07:13Z","timestamp":1643710033000},"page":"338-352","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Using Statistical and\u00a0Artificial Neural Networks Meta-learning Approaches for\u00a0Uncertainty Isolation in\u00a0Face Recognition by\u00a0the\u00a0Established Convolutional Models"],"prefix":"10.1007","author":[{"given":"Stanislav","family":"Selitskiy","sequence":"first","affiliation":[]},{"given":"Nikolaos","family":"Christou","sequence":"additional","affiliation":[]},{"given":"Natalya","family":"Selitskaya","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,2,2]]},"reference":[{"key":"26_CR1","unstructured":"Amodei, D., Olah, C., Steinhardt, J., Christiano, P., Schulman, J., Man\u00e9, D.: Concrete problems in AI safety (2016)"},{"key":"26_CR2","unstructured":"Andrychowicz, M., et al.: Learning to learn by gradient descent by gradient descent. In: Proceedings of the 30th International Conference on Neural Information Processing Systems, NIPS\u201916, pp. 3988\u20133996, Curran Associates Inc., Red Hook (2016)"},{"key":"26_CR3","unstructured":"Bergstra, J., Bardenet, R., Bengio, Y., K\u00e9gl, B.: Algorithms for hyper-parameter optimization. In: Advances in Neural Information Processing Systems, vol. 24. Curran Associates, Inc. (2011). https:\/\/proceedings.neurips.cc\/paper\/2011\/file\/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf"},{"key":"26_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-39756-6","volume-title":"Uncertainty: The Soul of Modeling, Probability & Statistics","author":"W Briggs","year":"2016","unstructured":"Briggs, W.: Uncertainty: The Soul of Modeling, Probability & Statistics. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-39756-6"},{"key":"26_CR5","volume-title":"Powers and Prospects: Reflections on Human Nature and the Social Order","author":"N Chomsky","year":"1996","unstructured":"Chomsky, N.: Powers and Prospects: Reflections on Human Nature and the Social Order. South End Press, Boston (1996)"},{"key":"26_CR6","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 70, pp. 1126\u20131135. PMLR, August 2017. http:\/\/proceedings.mlr.press\/v70\/finn17a.html"},{"key":"26_CR7","unstructured":"Fort, S., Ren, J., Lakshminarayanan, B.: Exploring the limits of out-of-distribution detection. CoRR arXiv:2106.03004 (2021)"},{"key":"26_CR8","unstructured":"Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Proceedings of the 33rd International Conference on International Conference on Machine Learning, ICML\u201916, vol. 48, pp. 1050\u20131059. JMLR.org (2016)"},{"issue":"7553","key":"26_CR9","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1038\/nature14541","volume":"521","author":"Z Ghahramani","year":"2015","unstructured":"Ghahramani, Z.: Probabilistic machine learning and artificial intelligence. Nature 521(7553), 452\u2013459 (2015). https:\/\/doi.org\/10.1038\/nature14541","journal-title":"Nature"},{"key":"26_CR10","unstructured":"Graves, A.: Practical variational inference for neural networks. In: Proceedings of the 24th International Conference on Neural Information Processing Systems, NIPS\u201911, pp. 2348\u20132356. Curran Associates Inc., Red Hook (2011)"},{"key":"26_CR11","unstructured":"Kendall, A., Gal, Y.: What uncertainties do we need in Bayesian deep learning for computer vision? arXiv:1703.04977 (2017)"},{"key":"26_CR12","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1007\/978-3-540-45224-9_24","volume-title":"Knowledge-Based Intelligent Information and Engineering Systems","author":"Z Kurd","year":"2003","unstructured":"Kurd, Z., Kelly, T.: Establishing safety criteria for artificial neural networks. In: Palade, V., Howlett, R.J., Jain, L. (eds.) KES 2003. LNCS (LNAI), vol. 2773, pp. 163\u2013169. Springer, Heidelberg (2003). https:\/\/doi.org\/10.1007\/978-3-540-45224-9_24"},{"key":"26_CR13","doi-asserted-by":"publisher","first-page":"e253","DOI":"10.1017\/S0140525X16001837","volume":"40","author":"BM Lake","year":"2017","unstructured":"Lake, B.M., Ullman, T.D., Tenenbaum, J.B., Gershman, S.J.: Building machines that learn and think like people. Behav. Brain Sci. 40, e253 (2017). https:\/\/doi.org\/10.1017\/S0140525X16001837","journal-title":"Behav. Brain Sci."},{"key":"26_CR14","unstructured":"Lakshminarayanan, B., Pritzel, A., Blundell, C.: Simple and scalable predictive uncertainty estimation using deep ensembles. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS\u201917, pp. 6405\u20136416. Curran Associates Inc., Red Hook (2017)"},{"key":"26_CR15","doi-asserted-by":"publisher","unstructured":"Liu, X., Wang, X., Matwin, S.: Interpretable deep convolutional neural networks via meta-learning. In: 2018 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20139 (2018). https:\/\/doi.org\/10.1109\/IJCNN.2018.8489172","DOI":"10.1109\/IJCNN.2018.8489172"},{"issue":"3","key":"26_CR16","doi-asserted-by":"publisher","first-page":"448","DOI":"10.1162\/neco.1992.4.3.448","volume":"4","author":"DJC MacKay","year":"1992","unstructured":"MacKay, D.J.C.: A practical Bayesian framework for backpropagation networks. Neural Comput. 4(3), 448\u2013472 (1992). https:\/\/doi.org\/10.1162\/neco.1992.4.3.448","journal-title":"Neural Comput."},{"issue":"4","key":"26_CR17","first-page":"12","volume":"27","author":"J McCarthy","year":"2006","unstructured":"McCarthy, J., Minsky, M.L., Rochester, N., Shannon, C.E.: A proposal for the Dartmouth summer research project on artificial intelligence, August 31, 1955. AI Mag. 27(4), 12 (2006)","journal-title":"AI Mag."},{"key":"26_CR18","doi-asserted-by":"publisher","unstructured":"Neal, R.M.: Bayesian Learning for Neural Networks. Lecture Notes in Statistics, vol.\u00a0118. Springer, New York (1996). https:\/\/doi.org\/10.1007\/978-1-4612-0745-0","DOI":"10.1007\/978-1-4612-0745-0"},{"key":"26_CR19","unstructured":"Nichol, A., Achiam, J., Schulman, J.: On first-order meta-learning algorithms. ArXiv arXiv:1803.02999 (2018)"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Postels, J., Ferroni, F., Coskun, H., Navab, N., Tombari, F.: Sampling-free epistemic uncertainty estimation using approximated variance propagation. CoRR arXiv:1908.00598 (2019)","DOI":"10.1109\/ICCV.2019.00302"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Ram, R., M\u00fcller, S., Pfreundt, F., Gauger, N., Keuper, J.: Scalable hyperparameter optimization with lazy Gaussian processes. In: 2019 IEEE\/ACM Workshop on Machine Learning in High Performance Computing Environments (MLHPC), pp. 56\u201365 (2019)","DOI":"10.1109\/MLHPC49564.2019.00011"},{"key":"26_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1007\/978-3-030-64580-9_49","volume-title":"Machine Learning, Optimization, and Data Science","author":"N Selitskaya","year":"2020","unstructured":"Selitskaya, N., Sielicki, S., Christou, N.: Challenges in real-life face recognition with heavy makeup and occlusions using deep learning algorithms. In: Nicosia, G., et al. (eds.) LOD 2020. LNCS, vol. 12566, pp. 600\u2013611. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-64580-9_49"},{"key":"26_CR23","doi-asserted-by":"publisher","unstructured":"Thrun, S., Pratt, L.: Learning to Learn. Springer, Boston (1998). https:\/\/doi.org\/10.1007\/978-1-4615-5529-2","DOI":"10.1007\/978-1-4615-5529-2"},{"key":"26_CR24","doi-asserted-by":"publisher","unstructured":"Turing, A.M.: I.-Computing machinery and intelligence. Mind LIX(236), 433\u2013460 (1950). https:\/\/doi.org\/10.1093\/mind\/LIX.236.433","DOI":"10.1093\/mind\/LIX.236.433"},{"key":"26_CR25","unstructured":"Vanschoren, J.: Meta-learning: a survey. ArXiv arXiv:1810.03548 (2018)"},{"key":"26_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-55583-2","volume-title":"Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops","year":"2020","unstructured":"Casimiro, A., Ortmeier, F., Schoitsch, E., Bitsch, F., Ferreira, P. (eds.): SAFECOMP 2020. LNCS, vol. 12235. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-55583-2"}],"container-title":["Lecture Notes in Computer Science","Machine Learning, Optimization, and Data Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-95470-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,2,1]],"date-time":"2022-02-01T10:15:47Z","timestamp":1643710547000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-95470-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030954697","9783030954703"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-95470-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"2 February 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LOD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning, Optimization, and Data Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Grasmere","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"mod2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/lod2021.icas.cc\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"215","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"86","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5-6","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1-2","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}