{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T02:41:37Z","timestamp":1769308897149,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["286525601"],"award-info":[{"award-number":["286525601"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Munich Center of Machine Learning (MCML)","award":["MCML22B"],"award-info":[{"award-number":["MCML22B"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,5,9]]},"DOI":"10.1145\/3575870.3587129","type":"proceedings-article","created":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T22:42:27Z","timestamp":1683585747000},"page":"1-13","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Automatic Abstraction Refinement in Neural Network Verification using Sensitivity Analysis"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4556-8308","authenticated-orcid":false,"given":"Tobias","family":"Ladner","sequence":"first","affiliation":[{"name":"School of Computation, Information and Technology, Technical University of Munich, Germany and Munich Center of Machine Learning (MCML), Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3733-842X","authenticated-orcid":false,"given":"Matthias","family":"Althoff","sequence":"additional","affiliation":[{"name":"School of Computation, Information and Technology, Technical University of Munich, Germany"}]}],"member":"320","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Matthias Althoff. 2010. Reachability analysis and its application to the safety assessment of autonomous cars. Ph.\u00a0D. Dissertation. Technische Universit\u00e4t M\u00fcnchen."},{"key":"e_1_3_2_1_2_1","volume-title":"Proc. of the Workshop on Applied Verification for Continuous and Hybrid Systems. 120\u2013151","author":"Althoff Matthias","year":"2015","unstructured":"Matthias Althoff. 2015. An introduction to CORA 2015. In Proc. of the Workshop on Applied Verification for Continuous and Hybrid Systems. 120\u2013151."},{"key":"e_1_3_2_1_3_1","volume-title":"The second international verification of neural networks competition (VNN-COMP 2021): Summary and results. arXiv preprint arXiv:2109.00498","author":"Bak Stanley","year":"2021","unstructured":"Stanley Bak, Changliu Liu, and Taylor\u00a0T. Johnson. 2021. The second international verification of neural networks competition (VNN-COMP 2021): Summary and results. arXiv preprint arXiv:2109.00498 (2021)."},{"key":"e_1_3_2_1_4_1","volume-title":"Measuring neural net robustness with constraints. Advances in Neural Information Processing Systems 29","author":"Bastani Osbert","year":"2016","unstructured":"Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, and Antonio Criminisi. 2016. Measuring neural net robustness with constraints. Advances in Neural Information Processing Systems 29 (2016)."},{"key":"e_1_3_2_1_5_1","first-page":"46","article-title":"Quadrotor dynamics and control","volume":"19","author":"Beard W.","year":"2008","unstructured":"Randal\u00a0W. Beard. 2008. Quadrotor dynamics and control. Brigham Young University 19, 3 (2008), 46\u201356.","journal-title":"Brigham Young University"},{"key":"e_1_3_2_1_6_1","volume-title":"Pattern recognition and machine learning. Vol.\u00a04","author":"Bishop M.","unstructured":"Christopher\u00a0M. Bishop and Nasser\u00a0M. Nasrabadi. 2006. Pattern recognition and machine learning. Vol.\u00a04. Springer."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302504.3311804"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5729"},{"key":"e_1_3_2_1_9_1","article-title":"Branch and bound for piecewise linear neural network verification","volume":"21","author":"Bunel Rudy","year":"2020","unstructured":"Rudy Bunel, P. Mudigonda, Ilker Turkaslan, Philip Torr, Jingyue Lu, and Pushmeet Kohli. 2020. Branch and bound for piecewise linear neural network verification. Journal of Machine Learning Research 21, 2020 (2020).","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/10722167_15"},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2018.00058"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-31954-2_19"},{"key":"e_1_3_2_1_13_1","volume-title":"International Conference on Learning Representations.","author":"Goodfellow Ian","year":"2015","unstructured":"Ian Goodfellow, Jonathon Shlens, and Christian Szegedy. 2015. Explaining and harnessing adversarial examples. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_14_1","volume-title":"ECAI","author":"Henriksen Patrick","year":"2020","unstructured":"Patrick Henriksen and Alessio Lomuscio. 2020. Efficient neural network verification via adaptive refinement and adversarial search. In ECAI 2020. IOS Press, 2513\u20132520."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/0893-6080(89)90020-8"},{"key":"e_1_3_2_1_16_1","volume-title":"POLAR: A polynomial arithmetic framework for verifying neural-betwork controlled systems. In Automated Technology for Verification and Analysis","author":"Huang Chao","year":"2022","unstructured":"Chao Huang, Jiameng Fan, Xin Chen, Wenchao Li, and Qi Zhu. 2022. POLAR: A polynomial arithmetic framework for verifying neural-betwork controlled systems. In Automated Technology for Verification and Analysis. Springer International Publishing, 414\u2013430."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63387-9_1"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-81685-8_11"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/1086837.1086847"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.29007\/kfk9"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63387-9_5"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-25540-4_26"},{"key":"e_1_3_2_1_23_1","unstructured":"Niklas Kochdumper. 2022. Extensions of polynomial zonotopes and their application to verification of cyber-physical systems. Ph.\u00a0D. Dissertation. Technische Universit\u00e4t M\u00fcnchen."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2020.3024348"},{"key":"e_1_3_2_1_25_1","volume-title":"Open- and closed-loop neural network verification using polynomial zonotopes. arXiv preprint arXiv:2207.02715","author":"Kochdumper Niklas","year":"2022","unstructured":"Niklas Kochdumper, Christian Schilling, Matthias Althoff, and Stanley Bak. 2022. Open- and closed-loop neural network verification using polynomial zonotopes. arXiv preprint arXiv:2207.02715 (2022)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3365365.3382192"},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-021-05973-0"},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1561\/9781680837872"},{"key":"e_1_3_2_1_29_1","first-page":"1611","article-title":"Verified global optimization with Taylor model-based range bounders","volume":"11","author":"Makino Kyoko","year":"2005","unstructured":"Kyoko Makino and Martin Berz. 2005. Verified global optimization with Taylor model-based range bounders. Transactions on Computers 11, 4 (2005), 1611\u20131618.","journal-title":"Transactions on Computers"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2021.102231"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3462308"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2012.09.545"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-14295-6_24"},{"key":"e_1_3_2_1_34_1","volume-title":"Handbook of discrete and combinatorial mathematics","author":"Rosen H","unstructured":"Kenneth\u00a0H Rosen. 2017. Handbook of discrete and combinatorial mathematics. CRC press."},{"key":"e_1_3_2_1_35_1","volume-title":"Learning representations by back-propagating errors. nature 323, 6088","author":"Rumelhart E.","year":"1986","unstructured":"David\u00a0E. Rumelhart, Geoffrey\u00a0E. Hinton, and Ronald\u00a0J. Williams. 1986. Learning representations by back-propagating errors. nature 323, 6088 (1986), 533\u2013536."},{"key":"e_1_3_2_1_36_1","volume-title":"Fast and effective robustness certification. Advances in Neural Information Processing Systems 31","author":"Singh Gagandeep","year":"2018","unstructured":"Gagandeep Singh, Timon Gehr, Matthew Mirman, Markus P\u00fcschel, and Martin Vechev. 2018. Fast and effective robustness certification. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_37_1","volume-title":"International Conference on Learning Representations.","author":"Singh Gagandeep","year":"2018","unstructured":"Gagandeep Singh, Timon Gehr, Markus P\u00fcschel, and Martin Vechev. 2018. Boosting robustness certification of neural networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/3290354"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.2307\/3029337"},{"key":"e_1_3_2_1_40_1","volume-title":"Ranking importance of input parameters of neural networks. Expert systems with Applications 15, 3-4","author":"Sung H.","year":"1998","unstructured":"Andrew\u00a0H. Sung. 1998. Ranking importance of input parameters of neural networks. Expert systems with Applications 15, 3-4 (1998), 405\u2013411."},{"key":"e_1_3_2_1_41_1","volume-title":"2nd International Conference on Learning Representations.","author":"Szegedy Christian","year":"2014","unstructured":"Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2014. Intriguing properties of neural networks. In 2nd International Conference on Learning Representations."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-53288-8_1"},{"key":"e_1_3_2_1_43_1","volume-title":"Beta-CROWN: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification. Advances in Neural Information Processing Systems 34","author":"Wang Shiqi","year":"2021","unstructured":"Shiqi Wang, Huan Zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, and J\u00a0Zico Kolter. 2021. Beta-CROWN: Efficient bound propagation with per-neuron split constraints for complete and incomplete neural network verification. Advances in Neural Information Processing Systems 34 (2021)."},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2808470"},{"key":"e_1_3_2_1_45_1","volume-title":"Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers. arXiv preprint arXiv:2011.13824","author":"Xu Kaidi","year":"2020","unstructured":"Kaidi Xu, Huan Zhang, Shiqi Wang, Yihan Wang, Suman Jana, Xue Lin, and Cho-Jui Hsieh. 2020. Fast and complete: Enabling complete neural network verification with rapid and massively parallel incomplete verifiers. arXiv preprint arXiv:2011.13824 (2020)."},{"key":"e_1_3_2_1_46_1","volume-title":"Efficient neural network robustness certification with general activation functions. Advances in Neural Information Processing Systems 31","author":"Zhang Huan","year":"2018","unstructured":"Huan Zhang, Tsui-Wei Weng, Pin-Yu Chen, Cho-Jui Hsieh, and Luca Daniel. 2018. Efficient neural network robustness certification with general activation functions. Advances in Neural Information Processing Systems 31 (2018)."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.1994.409622"}],"event":{"name":"HSCC '23: 26th ACM International Conference on Hybrid Systems: Computation and Control","location":"San Antonio TX USA","acronym":"HSCC '23","sponsor":["SIGBED ACM Special Interest Group on Embedded Systems"]},"container-title":["Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3575870.3587129","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3575870.3587129","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:46:12Z","timestamp":1750178772000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3575870.3587129"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,9]]},"references-count":47,"alternative-id":["10.1145\/3575870.3587129","10.1145\/3575870"],"URL":"https:\/\/doi.org\/10.1145\/3575870.3587129","relation":{},"subject":[],"published":{"date-parts":[[2023,5,9]]},"assertion":[{"value":"2023-05-09","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}