乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T20:10:02Z","timestamp":1732479002152,"version":"3.28.0"},"publisher-location":"Cham","reference-count":70,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031656293"},{"type":"electronic","value":"9783031656309"}],"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:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T00:00:00Z","timestamp":1721865600000},"content-version":"vor","delay-in-days":206,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"abstract":"Abstract<\/jats:title>This paper serves as a comprehensive system description of version 2.0 of the Marabou framework for formal analysis of neural networks. We discuss the tool\u2019s architectural design and highlight the major features and components introduced since its initial release.<\/jats:p>","DOI":"10.1007\/978-3-031-65630-9_13","type":"book-chapter","created":{"date-parts":[[2024,7,24]],"date-time":"2024-07-24T22:01:56Z","timestamp":1721858516000},"page":"249-264","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Marabou 2.0: A Versatile Formal Analyzer of\u00a0Neural Networks"],"prefix":"10.1007","author":[{"given":"Haoze","family":"Wu","sequence":"first","affiliation":[]},{"given":"Omri","family":"Isac","sequence":"additional","affiliation":[]},{"given":"Aleksandar","family":"Zelji\u0107","sequence":"additional","affiliation":[]},{"given":"Teruhiro","family":"Tagomori","sequence":"additional","affiliation":[]},{"given":"Matthew","family":"Daggitt","sequence":"additional","affiliation":[]},{"given":"Wen","family":"Kokke","sequence":"additional","affiliation":[]},{"given":"Idan","family":"Refaeli","sequence":"additional","affiliation":[]},{"given":"Guy","family":"Amir","sequence":"additional","affiliation":[]},{"given":"Kyle","family":"Julian","sequence":"additional","affiliation":[]},{"given":"Shahaf","family":"Bassan","sequence":"additional","affiliation":[]},{"given":"Pei","family":"Huang","sequence":"additional","affiliation":[]},{"given":"Ori","family":"Lahav","sequence":"additional","affiliation":[]},{"given":"Min","family":"Wu","sequence":"additional","affiliation":[]},{"given":"Min","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Ekaterina","family":"Komendantskaya","sequence":"additional","affiliation":[]},{"given":"Guy","family":"Katz","sequence":"additional","affiliation":[]},{"given":"Clark","family":"Barrett","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,7,25]]},"reference":[{"key":"13_CR1","unstructured":"Amir, G., et al.: Verifying Learning-Based Robotic Navigation Systems: Supplementary Video (2022). https:\/\/youtu.be\/QIZqOgxLkAE"},{"key":"13_CR2","doi-asserted-by":"crossref","unstructured":"Amir, G., et al.: Verifying learning-based robotic navigation systems. In: Proceedings of the 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), pp. 607\u2013627 (2023)","DOI":"10.1007\/978-3-031-30823-9_31"},{"key":"13_CR3","unstructured":"Amir, G., Schapira, M., Katz, G.: Towards scalable verification of deep reinforcement learning. In: Proceedings of the 21st International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 193\u2013203 (2021)"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Bak, S., Tran, H.D., Hobbs, K., Johnson, T.T.: Improved geometric path enumeration for verifying ReLU neural networks. In: International Conference on Computer Aided Verification, pp. 66\u201396. Springer (2020)","DOI":"10.1007\/978-3-030-53288-8_4"},{"key":"13_CR5","unstructured":"Balunovic, M., Baader, M., Singh, G., Gehr, T., Vechev, M.: Certifying geometric robustness of neural networks. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"13_CR6","doi-asserted-by":"publisher","unstructured":"Barrett, C., et al.: CVC4. In: Gopalakrishnan, G., Qadeer, S. (eds.) CAV 2011. LNCS, vol. 6806, pp. 171\u2013177. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-22110-1_14","DOI":"10.1007\/978-3-642-22110-1_14"},{"key":"13_CR7","unstructured":"Bassan, S., Amir, G., Corsi, D., Refaeli, I., Katz, G.: Formally explaining neural networks within reactive systems. In: Proceedings of the 23rd International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 10\u201322 (2023)"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Bassan, S., Katz, G.: Towards formal XAI: formally approximate minimal explanations of neural networks. In: Proceedings of the 29th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), pp. 187\u2013207 (2023)","DOI":"10.1007\/978-3-031-30823-9_10"},{"key":"13_CR9","doi-asserted-by":"publisher","unstructured":"Bauer-Marquart, F., Boetius, D., Leue, S., Schilling, C.: SpecRepair: counter-example guided safety repair of\u00a0deep neural networks. In: Legunsen, O., Rosu, G. (eds.) Model checking software: 28th International Symposium, SPIN 2022, Virtual Event, May 21, 2022, Proceedings, pp. 79\u201396. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-15077-7_5","DOI":"10.1007\/978-3-031-15077-7_5"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Boyd, S.P., Vandenberghe, L.: Convex Optimization. Cambridge University Press (2004)","DOI":"10.1017\/CBO9780511804441"},{"key":"13_CR11","doi-asserted-by":"crossref","unstructured":"Brix, C., Bak, S., Liu, C., Johnson, T.T.: The fourth international verification of neural networks competition (VNN-COMP 2023): summary and results. arXiv preprint arXiv:2312.16760 (2023)","DOI":"10.1007\/s10009-023-00703-4"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"Christakis, M., et al.: Automated safety verification of programs invoking neural networks. In: International Conference on Computer Aided Verification, pp. 201\u2013224. Springer (2021)","DOI":"10.1007\/978-3-030-81685-8_9"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"Cimatti, A., Griggio, A., Irfan, A., Roveri, M., Sebastiani, R.: Incremental linearization for satisfiability and verification modulo nnlinear arithmetic and transcendental functions. ACM Trans. Computat. Logic 19(3), 1\u201352 (2018)","DOI":"10.1145\/3230639"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Desmartin, R., Isac, O., Passmore, G., Stark, K., Komendantskaya, E., Katz, G.: Towards a certified proof checker for deep neural network verification. In: Proceedings of the 33rd International Symposium on Logic-Based Program Synthesis and Transformation (LOPSTR), pp. 198\u2013209 (2023)","DOI":"10.1007\/978-3-031-45784-5_13"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Eliyahu, T., Kazak, Y., Katz, G., Schapira, M.: Verifying learning-augmented systems. In: Proceedings of the Conference of the ACM Special Interest Group on Data Communication on the Applications, Technologies, Architectures, and Protocols for Computer Communication (SIGCOMM), pp. 305\u2013318 (2021)","DOI":"10.1145\/3452296.3472936"},{"key":"13_CR16","unstructured":"Ferrari, C., Mueller, M.N., Jovanovi\u0107, N., Vechev, M.: Complete verification via multi-neuron relaxation guided branch-and-bound. In: International Conference on Learning Representations (2022)"},{"key":"13_CR17","doi-asserted-by":"crossref","unstructured":"Funk, N., Baumann, D., Berenz, V., Trimpe, S.: Learning event-triggered control from data through joint optimization. IFAC J. Syst. Control 16 (2021)","DOI":"10.1016\/j.ifacsc.2021.100144"},{"key":"13_CR18","unstructured":"Geng, C., Le, N., Xu, X., Wang, Z., Gurfinkel, A., Si, X.: Towards reliable neural specifications. In: International Conference on Machine Learning, pp. 11196\u201311212. PMLR (2023)"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Gopinath, D., Converse, H., Pasareanu, C., Taly, A.: Property inference for deep neural networks. In: 2019 34th IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 797\u2013809. IEEE (2019)","DOI":"10.1109\/ASE.2019.00079"},{"key":"13_CR20","unstructured":"Gowal, S., et al.: On the effectiveness of interval bound popagation for training verifiably robust models. arXiv preprint arXiv:1810.12715 (2018)"},{"key":"13_CR21","unstructured":"Graph Neural Networks support in ONNX (2022). https:\/\/github.com\/microsoft\/onnxruntime\/issues\/12103"},{"key":"13_CR22","unstructured":"Guidotti, D., Leofante, F., Pulina, L., Tacchella, A.: Verification of neural nNetworks: enhancing scalability through pruning. In: European Conference on Artificial Intelligence, pp. 2505\u20132512. IOS Press (2020)"},{"key":"13_CR23","unstructured":"Gurobi Optimization, LLC: Gurobi Optimizer Reference Manual (2023). https:\/\/www.gurobi.com"},{"key":"13_CR24","doi-asserted-by":"crossref","unstructured":"Henriksen, P., Lomuscio, A.: DEEPSPLIT: an eEfficient splitting method for neural network verification via indirect effect analysis. In: International Joint Conference on Artificial Intelligence, pp. 2549\u20132555. ijcai.org (2021)","DOI":"10.24963\/ijcai.2021\/351"},{"key":"13_CR25","unstructured":"Henriksen, P., Lomuscio, A.R.: Efficient neural network verification via adaptive refinement and adversarial search. In: Giacomo, G.D., et al. (eds.) European Conference on Artificial Intelligence, vol.\u00a0325, pp. 2513\u20132520. IOS Press (2020)"},{"key":"13_CR26","unstructured":"Huang, X., Marques-Silva, J.: From robustness to explainability and back again. arXiv preprint arXiv:2306.03048 (2023)"},{"key":"13_CR27","doi-asserted-by":"publisher","unstructured":"Huang, X., Liu, M.-Y., Belongie, S., Kautz, J.: Multimodal unsupervised image-to-image translation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11207, pp. 179\u2013196. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01219-9_11","DOI":"10.1007\/978-3-030-01219-9_11"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Ignatiev, A., Narodytska, N., Marques-Silva, J.: Abduction-based explanations for machine learning models. In: AAAI Conference on Artificial Intelligence, vol.\u00a033, pp. 1511\u20131519. AAAI Press (2019)","DOI":"10.1609\/aaai.v33i01.33011511"},{"key":"13_CR29","unstructured":"Isac, O., Barrett, C., Zhang, M., Katz, G.: Neural network verification with proof production. In: Proceedings of the 22nd International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 38\u201348 (2022)"},{"issue":"3","key":"13_CR30","doi-asserted-by":"publisher","first-page":"598","DOI":"10.2514\/1.G003724","volume":"42","author":"K Julian","year":"2019","unstructured":"Julian, K., Kochenderfer, M., Owen, M.: Deep neural network compression for aircraft collision avoidance systems. J. Guid. Control. Dyn. 42(3), 598\u2013608 (2019)","journal-title":"J. Guid. Control. Dyn."},{"key":"13_CR31","doi-asserted-by":"publisher","unstructured":"Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kun\u010dak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 97\u2013117. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-63387-9_5","DOI":"10.1007\/978-3-319-63387-9_5"},{"key":"13_CR32","doi-asserted-by":"publisher","unstructured":"Katz, G., et al.: The Marabou framework for verification and analysis of deep neural networks. In: Dillig, I., Tasiran, S. (eds.) CAV 2019. LNCS, vol. 11561, pp. 443\u2013452. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-25540-4_26","DOI":"10.1007\/978-3-030-25540-4_26"},{"issue":"9","key":"13_CR33","first-page":"574","volume":"19","author":"SM Katz","year":"2022","unstructured":"Katz, S.M., Corso, A.L., Strong, C.A., Kochenderfer, M.J.: Verification of image-based neural network controllers using generative models. J. Aerosp. Inf. Syst. 19(9), 574\u2013584 (2022)","journal-title":"J. Aerosp. Inf. Syst."},{"key":"13_CR34","doi-asserted-by":"publisher","unstructured":"Liu, C., Cofer, D., Osipychev, D. Verifying an\u00a0aircraft collision avoidance neural network with\u00a0Marabou. In: Rozier, K.Y., Chaudhuri, S. (eds.) NFM 2023. LNCS, pp. 79\u201385. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-33170-1_5","DOI":"10.1007\/978-3-031-33170-1_5"},{"key":"13_CR35","doi-asserted-by":"publisher","unstructured":"Lopez, D.M., Choi, S.W., Tran, H.-D., Johnson, T.T.: NNV 2.0: the neural network verification tool. In: Enea, C., Lal, A. (eds.) CAV 2023, pp. 397\u2013412. Springer, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-37703-7_19","DOI":"10.1007\/978-3-031-37703-7_19"},{"key":"13_CR36","unstructured":"Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Malfa, E.L., Michelmore, R., Zbrzezny, A.M., Paoletti, N., Kwiatkowska, M.: On guaranteed optimal robust explanations for NLP models. In: International Joint Conference on Artificial Intelligence, pp. 2658\u20132665. ijcai.org (2021)","DOI":"10.24963\/ijcai.2021\/366"},{"key":"13_CR38","unstructured":"Matheson, R.: AI system optimally allocates workloads across thousands of servers to cut costs, save energy. Tech Xplore (2019). https:\/\/techxplore.com\/news\/2019-08-ai-optimally-allocates-workloads-thousands.html"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Mirman, M., H\u00e4gele, A., Bielik, P., Gehr, T., Vechev, M.: Robustness certification with generative models. In: ACM SIGPLAN International Conference on Programming Language Design and Implementation, pp. 1141\u20131154 (2021)","DOI":"10.1145\/3410308"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Mohapatra, J., Weng, T.W., Chen, P.Y., Liu, S., Daniel, L.: Towards verifying robustness of neural networks against a family of semantic perturbations. In: IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 244\u2013252 (2020)","DOI":"10.1109\/CVPR42600.2020.00032"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"M\u00fcller, M.N., Makarchuk, G., Singh, G., P\u00fcschel, M., Vechev, M.: Prima: general and precise neural network certification via scalable convex hull approximations. Proc. ACM Program. Lang. 6(POPL), 1\u201333 (2022)","DOI":"10.1145\/3498704"},{"key":"13_CR42","doi-asserted-by":"publisher","unstructured":"Paterson, C., et al.: DeepCert: verification of contextually relevant robustness for neural network image classifiers. In: Habli, I., Sujan, M., Bitsch, F. (eds.) SAFECOMP 2021. LNCS, pp. 3\u201317. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-83903-1_5","DOI":"10.1007\/978-3-030-83903-1_5"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Why should i trust you? Explaining the predictions of any classifier. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1135\u20131144 (2016)","DOI":"10.1145\/2939672.2939778"},{"key":"13_CR44","doi-asserted-by":"crossref","unstructured":"Ribeiro, M.T., Singh, S., Guestrin, C.: Anchors: high-precision model-agnostic explanations. In: AAAI Conference on Artificial Intelligence, vol.\u00a032, pp. 1527\u20131535. AAAI Press (2018)","DOI":"10.1609\/aaai.v32i1.11491"},{"key":"13_CR45","first-page":"15098","volume":"32","author":"G Singh","year":"2019","unstructured":"Singh, G., Ganvir, R., P\u00fcschel, M., Vechev, M.: Beyond the single neuron convex barrier for neural network certification. Adv. Neural. Inf. Process. Syst. 32, 15098\u201315109 (2019)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"13_CR46","doi-asserted-by":"crossref","unstructured":"Singh, G., Gehr, T., P\u00fcschel, M., Vechev, M.: An abstract domain for certifying neural networks. Proc. ACM Program. Lang. 3(POPL), 1\u201330 (2019)","DOI":"10.1145\/3290354"},{"key":"13_CR47","unstructured":"Singh, G., Gehr, T., P\u00fcschel, M., Vechev, M.: Boosting robustness certification of neural networks. In: International Conference on Learning Representations (2019)"},{"key":"13_CR48","doi-asserted-by":"crossref","unstructured":"Strong, C., et al.: Global optimization of objective functions represented by ReLU networks. J. Mach. Learn. 112(10), 3685\u20133712 (2021)","DOI":"10.1007\/s10994-021-06050-2"},{"key":"13_CR49","doi-asserted-by":"publisher","unstructured":"Sun, Y., Usman, M., Gopinath, D., P\u0103s\u0103reanu, C.S.: VPN: verification of\u00a0poisoning in\u00a0neural networks. In: Isac, O., Ivanov, R., Katz, G., Narodytska, N., Nenzi, L. (eds.) Software Verification and Formal Methods for ML-Enabled Autonomous Systems: 5th International Workshop, FoMLAS 2022, and 15th International Workshop, NSV 2022, Haifa, 31 July\u20131 August, and 11 August 2022, Proceedings, pp. 3\u201314. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-21222-2_1","DOI":"10.1007\/978-3-031-21222-2_1"},{"key":"13_CR50","unstructured":"Tjeng, V., Xiao, K.Y., Tedrake, R.: Evaluating robustness of neural networks with mixed integer programming. In: International Conference on Learning Representations (2019)"},{"key":"13_CR51","doi-asserted-by":"crossref","unstructured":"Tran, H.D., et al.: NNV: the neural network verification tool for deep neural networks and learning-enabled cyber-physical systems. In: International Conference on Computer Aided Verification, pp. 3\u201317. Springer (2020)","DOI":"10.1007\/978-3-030-53288-8_1"},{"key":"13_CR52","doi-asserted-by":"crossref","unstructured":"Vanderbei, R.: Linear programming: foundations and extensions. J. Oper. Res. Soc. (1998)","DOI":"10.1038\/sj.jors.2600987"},{"key":"13_CR53","unstructured":"Vaswani, A., et al.: Attention is all nou need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"13_CR54","doi-asserted-by":"crossref","unstructured":"Vinzent, M., Sharma, S., Hoffmann, J.: Neural policy safety verification via predicate abstraction: CEGAR. In: AAAI Conference on Artificial Intelligence, pp. 15188\u201315196. AAAI Press (2023)","DOI":"10.1609\/aaai.v37i12.26772"},{"key":"13_CR55","unstructured":"Wang, S., Pei, K., Whitehouse, J., Yang, J., Jana, S.: Efficient formal safety analysis of neural networks. Adv. Neural. Inf. Process. Syst. 31, 6369\u20136379 (2018)"},{"key":"13_CR56","unstructured":"Wang, S., et al.: Beta-crown: efficient bound propagation with per-neuron split constraints for neural network robustness verification. Adv. Neural. Inf. Process. Syst. 34, 29909\u201329921 (2021)"},{"key":"13_CR57","unstructured":"Wu, H., et al.: Parallelization techniques for verifying neural networks. In: Proceedings of the 20th International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 128\u2013137 (2020)"},{"key":"13_CR58","doi-asserted-by":"crossref","unstructured":"Wu, H., Zelji\u0107, A., Katz, G., Barrett, C.: Efficient neural network analysis with sum-of-infeasibilities. In: Proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems (TACAS), pp. 143\u2013163 (2022)","DOI":"10.1007\/978-3-030-99524-9_8"},{"key":"13_CR59","doi-asserted-by":"crossref","unstructured":"Wu, H., Barrett, C., Sharif, M., Narodytska, N., Singh, G.: Scalable verification of GNN-based job schedulers. Proc. ACM Program. Lang. 6(OOPSLA), 1036\u20131065 (2022)","DOI":"10.1145\/3563325"},{"key":"13_CR60","doi-asserted-by":"publisher","unstructured":"Wu, H., et\u00a0al.: Artifact for Marabou 2.0: a versatile formal analyzer of neural networks (2022). https:\/\/doi.org\/10.5281\/zenodo.11116016","DOI":"10.5281\/zenodo.11116016"},{"key":"13_CR61","unstructured":"Wu, H., et\u00a0al.: Marabou 2.0: a versatile formal analyzer of neural networks. arXiv preprint arXiv:2401.14461 (2024)"},{"key":"13_CR62","doi-asserted-by":"crossref","unstructured":"Wu, H., et al.: Toward certified robustness against real-world distribution shifts. In: IEEE Conference on Secure and Trustworthy Machine Learning, pp. 537\u2013553. IEEE (2023)","DOI":"10.1109\/SaTML54575.2023.00042"},{"key":"13_CR63","unstructured":"Wu, M., Wu, H., Barrett, C.: VeriX: towards verified explainability of deep neural networks. Adv. Neural Inf. Process. Syst. (2022)"},{"key":"13_CR64","doi-asserted-by":"crossref","unstructured":"Xie, X., Kersting, K., Neider, D.: Neuro-symbolic verification of deep neural networks. In: International Joint Conferences on Artificial Intelligence, pp. 3622\u20133628. ijcai.org (2022)","DOI":"10.24963\/ijcai.2022\/503"},{"key":"13_CR65","unstructured":"Xu, K., et al.: Automatic perturbation analysis for scalable certified robustness and beyond. Adv. Neural. Inf. Process. Syst. 33, 1129\u20131141 (2020)"},{"key":"13_CR66","doi-asserted-by":"crossref","unstructured":"Yerushalmi, R.: Enhancing deep reinforcement learning with executable specifications. In: International Conference on Software Engineering, pp. 213\u2013217. IEEE (2023)","DOI":"10.1109\/ICSE-Companion58688.2023.00058"},{"key":"13_CR67","unstructured":"Yosinski, J., Clune, J., Nguyen, A., Fuchs, T., Lipson, H.: Understanding neural networks through deep visualization. arXiv preprint arXiv:1506.06579 (2015)"},{"key":"13_CR68","unstructured":"Zelazny, T., Wu, H., Barrett, C., Katz, G.: On reducing over-approximation errors for neural network verification. In: Proceedings of the 22nd International Conference on Formal Methods in Computer-Aided Design (FMCAD), pp. 17\u201326 (2022)"},{"key":"13_CR69","unstructured":"Zhang, H., et al.: General cutting planes for bound-propagation-based neural network verification. Adv. Neural. Inf. Process. Syst. 35, 1656\u20131670 (2022)"},{"key":"13_CR70","unstructured":"Zhang, H., Weng, T.W., Chen, P.Y., Hsieh, C.J., Daniel, L.: Efficient neural network robustness certification with general activation functions. Adv. Neural. Inf. Process. Syst. 31, 4944\u20134953 (2018)"}],"container-title":["Lecture Notes in Computer Science","Computer Aided Verification"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-65630-9_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,24]],"date-time":"2024-11-24T19:47:43Z","timestamp":1732477663000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-65630-9_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031656293","9783031656309"],"references-count":70,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-65630-9_13","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":"25 July 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CAV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computer Aided Verification","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Montreal, QC","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Canada","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"36","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cav2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/i-cav.org\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}