{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T16:05:45Z","timestamp":1743005145713,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031660634"},{"type":"electronic","value":"9783031660641"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-66064-1_2","type":"book-chapter","created":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T10:02:00Z","timestamp":1721988120000},"page":"20-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Safety Performance of\u00a0Neural Networks in\u00a0the\u00a0Presence of\u00a0Covariate Shift"],"prefix":"10.1007","author":[{"given":"Chih-Hong","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Harald","family":"Ruess","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Konstantinos","family":"Theodorou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,7,27]]},"reference":[{"key":"2_CR1","unstructured":"ANSI\/UL 4600: Standard For Evaluation of Autonomous Products. Standard (2020)"},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"251","DOI":"10.1007\/978-3-319-68167-2_18","volume-title":"Automated Technology for Verification and Analysis","author":"C-H Cheng","year":"2017","unstructured":"Cheng, C.-H., N\u00fchrenberg, G., Ruess, H.: Maximum resilience of artificial neural networks. In: D\u2019Souza, D., Narayan Kumar, K. (eds.) ATVA 2017. LNCS, vol. 10482, pp. 251\u2013268. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-68167-2_18"},{"key":"2_CR3","volume-title":"Marjory S Blumenthal, James M Anderson, and Nidhi Kalra","author":"L Fraade-Blanar","year":"2018","unstructured":"Fraade-Blanar, L.: Marjory S Blumenthal, James M Anderson, and Nidhi Kalra. Forging a framework, Measuring automated vehicle safety (2018)"},{"key":"2_CR4","unstructured":"Gao, S., Ver\u00a0Steeg, G., Galstyan, A.: Efficient estimation of mutual information for strongly dependent variables. In: Artificial Intelligence and Statistics, pp. 277\u2013286. PMLR (2015)"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Gehr, T., et al.: Ai2: safety and robustness certification of neural networks with abstract interpretation. In: Proceedings of the 2018 IEEE Symposium on Security and Privacy (SP), pp. 3\u201318. IEEE (2018)","DOI":"10.1109\/SP.2018.00058"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Gowal, S., et al.: Scalable verified training for provably robust image classification. In: Proceedings of the 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 4842\u20134851 (2019)","DOI":"10.1109\/ICCV.2019.00494"},{"key":"2_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1007\/978-3-030-55583-2_26","volume-title":"Computer Safety, Reliability, and Security. SAFECOMP 2020 Workshops","author":"P Koopman","year":"2020","unstructured":"Koopman, P., Wagner, M.: Positive trust balance for self-driving car deployment. In: Casimiro, A., Ortmeier, F., Schoitsch, E., Bitsch, F., Ferreira, P. (eds.) SAFECOMP 2020. LNCS, vol. 12235, pp. 351\u2013357. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-55583-2_26"},{"key":"2_CR8","unstructured":"Kouw, W.M., Loog, M.: An introduction to domain adaptation and transfer learning. arXiv preprint arXiv:1812.11806 (2019)"},{"issue":"6","key":"2_CR9","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevE.69.066138","volume":"69","author":"A Kraskov","year":"2004","unstructured":"Kraskov, A., St\u00f6gbauer, H., Grassberger, P.: Estimating mutual information. Phys. Rev. E 69(6), 066138 (2004)","journal-title":"Phys. Rev. E"},{"key":"2_CR10","unstructured":"LeCun, Y.: The MNIST database of handwritten digits (1998). http:\/\/yann.lecun.com\/exdb\/mnist\/"},{"key":"2_CR11","unstructured":"Lust, J., Condurache, A.P.: A survey on assessing the generalization envelope of deep neural networks: predictive uncertainty, out-of-distribution and adversarial samples. arXiv preprint arXiv:2008.09381 (2020)"},{"key":"2_CR12","unstructured":"McAllester, D., Stratos, K.: Formal limitations on the measurement of mutual information. In: International Conference on Artificial Intelligence and Statistics, pp. 875\u2013884. PMLR (2020)"},{"key":"2_CR13","unstructured":"Paszke, A., et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. In: Advances in Neural Information Processing Systems (NeurIPS), vol. 32 (2019)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Quinonero-Candela, J., Sugiyama, M., Schwaighofer, A., Lawrence, N.D.: Dataset Shift in Machine Learning. MIT Press (2008)","DOI":"10.7551\/mitpress\/9780262170055.001.0001"},{"issue":"5","key":"2_CR15","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1109\/JPROC.2021.3052449","volume":"109","author":"L Ruff","year":"2021","unstructured":"Ruff, L., et al.: A unifying review of deep and shallow anomaly detection. Proc. IEEE 109(5), 756\u2013795 (2021)","journal-title":"Proc. IEEE"},{"key":"2_CR16","unstructured":"Vogel, R., Achab, M., Cl\u00e9men\u00e7on, S., Tillier, C.: Weighted empirical risk minimization: sample selection bias correction based on importance sampling. arXiv preprint arXiv:2002.05145 (2020)"}],"container-title":["Lecture Notes in Computer Science","Verified Software. Theories, Tools and Experiments"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-66064-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,26]],"date-time":"2024-07-26T10:02:14Z","timestamp":1721988134000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-66064-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031660634","9783031660641"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-66064-1_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"27 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"VSTTE","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Verified Software: Theories, Tools, and Experiments","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ames, IA","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"USA","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"vstte2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/homepage.cs.uiowa.edu\/~ajreynol\/VSTTE2023\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}