{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T18:27:50Z","timestamp":1743100070046,"version":"3.40.3"},"publisher-location":"Cham","reference-count":31,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031212215"},{"type":"electronic","value":"9783031212222"}],"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.springernature.com\/gp\/researchers\/text-and-data-mining"},{"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.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21222-2_3","type":"book-chapter","created":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T09:04:55Z","timestamp":1671095095000},"page":"29-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CEG4N: Counter-Example Guided Neural Network Quantization Refinement"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8380-6058","authenticated-orcid":false,"suffix":"Jr.","given":"Jo\u00e3o Batista P.","family":"Matos","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6603-3476","authenticated-orcid":false,"given":"Iury","family":"Bessa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0028-5440","authenticated-orcid":false,"given":"Edoardo","family":"Manino","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2612-6296","authenticated-orcid":false,"given":"Xidan","family":"Song","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6235-4272","authenticated-orcid":false,"given":"Lucas C.","family":"Cordeiro","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,12,16]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Abate, A., et al.: Sound and automated synthesis of digital stabilizing controllers for continuous plants. In: Frehse, G., Mitra, S. (eds.) Proceedings of the 20th International Conference on Hybrid Systems: Computation and Control, HSCC 2017, Pittsburgh, PA, USA, 18\u201320 April 2017, pp. 197\u2013206. ACM (2017). https:\/\/doi.org\/10.1145\/3049797.3049802","DOI":"10.1145\/3049797.3049802"},{"key":"3_CR2","doi-asserted-by":"publisher","unstructured":"Abiodun, O.I., Jantan, A., Omolara, A.E., Dada, K.V., Mohamed, N.A., Arshad, H.: State-of-the-art in artificial neural network applications: a survey. Heliyon. 4(11), e00938 (2018). https:\/\/doi.org\/10.1016\/j.heliyon.2018.e00938, https:\/\/www.sciencedirect.com\/science\/article\/pii\/S2405844018332067","DOI":"10.1016\/j.heliyon.2018.e00938"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Albarghouthi, A.: Introduction to neural network verification. arXiv:2109.10317 (2021)","DOI":"10.1561\/9781680839111"},{"key":"3_CR4","unstructured":"Bai, J., Lu, F., Zhang, K., et al.: ONNX: Open neural network exchange (2019). https:\/\/github.com\/onnx\/onnx"},{"key":"3_CR5","unstructured":"Bak, S., Liu, C., Johnson, T.: The second international verification of neural networks competition (VNN-COMP 2021): Summary and results (2021)"},{"key":"3_CR6","unstructured":"Bojarski, M., et al.: End to end learning for self-driving cars. arXiv:1604.07316 (2016)"},{"key":"3_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1007\/978-3-030-58475-7_50","volume-title":"Principles and Practice of Constraint Programming","author":"M Kleine B\u00fcning","year":"2020","unstructured":"Kleine B\u00fcning, M., Kern, P., Sinz, C.: Verifying equivalence properties of neural networks with ReLU activation functions. In: Simonis, H. (ed.) CP 2020. LNCS, vol. 12333, pp. 868\u2013884. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58475-7_50"},{"key":"3_CR8","doi-asserted-by":"publisher","unstructured":"Charytanowicz, M., Niewczas, J., Kulczycki, P., Kowalski, P.A., \u0141ukasik, S., \u017bak, S.: Complete gradient clustering algorithm for features analysis of x-ray images. In: Pi\u0229tka, E., Kawa, J. (eds) Information Technologies in Biomedicine. AINSC, vol. 69, pp. 15\u201324. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-13105-9_2","DOI":"10.1007\/978-3-642-13105-9_2"},{"key":"3_CR9","unstructured":"Cheng, Y., Wang, D., Zhou, P., Zhang, T.: A survey of model compression and acceleration for deep neural networks. arXiv:1710.09282 (2017)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Eleftheriadis, C., Kekatos, N., Katsaros, P., Tripakis, S.: On neural network equivalence checking using SMT solvers. arXiv:2203.11629 (2022)","DOI":"10.1007\/978-3-031-15839-1_14"},{"key":"3_CR11","unstructured":"Esser, S.K., Appuswamy, R., Merolla, P., Arthur, J.V., Modha, D.S.: Backpropagation for energy-efficient neuromorphic computing. In: Cortes, C., Lawrence, N., Lee, D., Sugiyama, M., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 28. Curran Associates, Inc. (2015). https:\/\/proceedings.neurips.cc\/paper\/2015\/file\/10a5ab2db37feedfdeaab192ead4ac0e-Paper.pdf"},{"key":"3_CR12","doi-asserted-by":"crossref","unstructured":"Farabet, C., et al.: Large-scale FPGA-based convolutional networks (2011)","DOI":"10.1017\/CBO9781139042918.020"},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1111\/j.1469-1809.1936.tb02137.x","volume":"7","author":"RA Fisher","year":"1936","unstructured":"Fisher, R.A.: The use of multiple measurements in taxonomic problems. Ann. Eugen. 7, 179\u2013188 (1936)","journal-title":"Ann. Eugen."},{"issue":"6","key":"3_CR14","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s10009-020-00571-2","volume":"23","author":"MR Gadelha","year":"2020","unstructured":"Gadelha, M.R., Menezes, R.S., Cordeiro, L.C.: ESBMC 6.1: automated test case generation using bounded model checking. Int. J. Softw. Tools Technol. Transf. 23(6), 857\u2013861 (2020). https:\/\/doi.org\/10.1007\/s10009-020-00571-2","journal-title":"Int. J. Softw. Tools Technol. Transf."},{"key":"3_CR15","doi-asserted-by":"publisher","unstructured":"Gadelha, M.R., Monteiro, F.R., Morse, J., Cordeiro, L.C., Fischer, B., Nicole, D.A.: ESBMC 5.0: an industrial-strength C model checker. In: 2018 33rd IEEE\/ACM International Conference on Automated Software Engineering (ASE), pp. 888\u2013891 (2018). https:\/\/doi.org\/10.1145\/3238147.3240481","DOI":"10.1145\/3238147.3240481"},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Gholami, A., Kim, S., Dong, Z., Yao, Z., Mahoney, M.W., Keutzer, K.: A survey of quantization methods for efficient neural network inference. arXiv:2103.13630 (2022)","DOI":"10.1201\/9781003162810-13"},{"key":"3_CR17","unstructured":"Han, S., Pool, J., Tran, J., Dally, W.J.: Learning both weights and connections for efficient neural network. arXiv:1506.02626 (2015)"},{"key":"3_CR18","unstructured":"Hooker, S., Courville, A.C., Dauphin, Y., Frome, A.: Selective brain damage: measuring the disparate impact of model pruning. arXiv:1911.05248 (2019)"},{"key":"3_CR19","unstructured":"Huang, X., et al.: Safety and trustworthiness of deep neural networks: a survey. arXiv:1812.08342 (2018)"},{"key":"3_CR20","unstructured":"Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Quantized neural networks: training neural networks with low precision weights and activations. arXiv:1609.07061 (2017)"},{"key":"3_CR21","doi-asserted-by":"publisher","unstructured":"IEEE: IEEE standard for floating-point arithmetic. IEEE Std. 754\u20132019 (Revision of IEEE 754\u20132008), pp. 1\u201384 (2019). https:\/\/doi.org\/10.1109\/IEEESTD.2019.8766229","DOI":"10.1109\/IEEESTD.2019.8766229"},{"key":"3_CR22","unstructured":"Jacob, B., et al.: Quantization and training of neural networks for efficient integer-arithmetic-only inference. CoRR abs\/1712.05877 (2017). arxiv:1712.05877"},{"key":"3_CR23","doi-asserted-by":"publisher","unstructured":"Julian, K.D., Lopez, J., Brush, J.S., Owen, M.P., Kochenderfer, M.J.: Policy compression for aircraft collision avoidance systems. In: 2016 IEEE\/AIAA 35th Digital Avionics Systems Conference (DASC), pp. 1\u201310 (2016). https:\/\/doi.org\/10.1109\/DASC.2016.7778091","DOI":"10.1109\/DASC.2016.7778091"},{"key":"3_CR24","doi-asserted-by":"publisher","unstructured":"Kirchhoffer, H., et al.: Overview of the neural network compression and representation (NNR) standard. IEEE Trans. Circ. Syst. Video Technol. 1 (2021). https:\/\/doi.org\/10.1109\/TCSVT.2021.3095970","DOI":"10.1109\/TCSVT.2021.3095970"},{"key":"3_CR25","unstructured":"Krishnamoorthi, R.: Quantizing deep convolutional networks for efficient inference: a whitepaper. CoRR abs\/1806.08342 (2018). arxiv:1806.08342"},{"key":"3_CR26","unstructured":"LeCun, Y., Cortes, C.: The MNIST database of handwritten digits (2005)"},{"key":"3_CR27","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1561\/2400000035","volume":"4","author":"C Liu","year":"2021","unstructured":"Liu, C., Arnon, T., Lazarus, C., Barrett, C.W., Kochenderfer, M.J.: Algorithms for verifying deep neural networks. Found. Trends Optim. 4, 244\u2013404 (2021)","journal-title":"Found. Trends Optim."},{"key":"3_CR28","doi-asserted-by":"crossref","unstructured":"Narodytska, N., Kasiviswanathan, S.P., Ryzhyk, L., Sagiv, S., Walsh, T.: Verifying properties of binarized deep neural networks. In: AAAI (2018)","DOI":"10.1609\/aaai.v32i1.12206"},{"key":"3_CR29","unstructured":"Paszke, A., et al.: Pytorch: an imperative style, high-performance deep learning library. In: Wallach, H., Larochelle, H., Beygelzimer, A., d\u2019Alch\u00e9-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 8024\u20138035. Curran Associates, Inc. (2019). http:\/\/papers.neurips.cc\/paper\/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf"},{"key":"3_CR30","doi-asserted-by":"publisher","unstructured":"Teuber, S., Buning, M.K., Kern, P., Sinz, C.: Geometric path enumeration for equivalence verification of neural networks. In: 2021 IEEE 33rd International Conference on Tools with Artificial Intelligence (ICTAI), November 2021. https:\/\/doi.org\/10.1109\/ictai52525.2021.00035, http:\/\/dx.doi.org\/10.1109\/ICTAI52525.2021.00035","DOI":"10.1109\/ictai52525.2021.00035"},{"key":"3_CR31","unstructured":"Zhang, J., Zhou, Y., Saab, R.: Post-training quantization for neural networks with provable guarantees. arXiv preprint arXiv:2201.11113 (2022)"}],"container-title":["Lecture Notes in Computer Science","Software Verification and Formal Methods for ML-Enabled Autonomous Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21222-2_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,12,15]],"date-time":"2022-12-15T09:05:41Z","timestamp":1671095141000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21222-2_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031212215","9783031212222"],"references-count":31,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21222-2_3","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":"16 December 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"FoMLAS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Workshop on Formal Methods for ML-Enabled Autonomous Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Haifa","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31 July 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 August 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"fomlas2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"8","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":"8","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":"100% - 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":"2","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":"2.5","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}