{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T00:35:20Z","timestamp":1742949320000,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030589417"},{"type":"electronic","value":"9783030589424"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-58942-4_24","type":"book-chapter","created":{"date-parts":[[2020,9,18]],"date-time":"2020-09-18T06:03:58Z","timestamp":1600409038000},"page":"364-380","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid Classification and Reasoning for Image-Based Constraint Solving"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9122-926X","authenticated-orcid":false,"given":"Maxime","family":"Mulamba","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8675-8178","authenticated-orcid":false,"given":"Jayanta","family":"Mandi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1810-082X","authenticated-orcid":false,"given":"Rocsildes","family":"Canoy","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2156-2155","authenticated-orcid":false,"given":"Tias","family":"Guns","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,19]]},"reference":[{"key":"24_CR1","unstructured":"Amos, B., Kolter, J.Z.: Optnet: differentiable optimization as a layer in neural networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 136\u2013145. JMLR.org (2017)"},{"issue":"04","key":"24_CR2","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1142\/S0129065797000422","volume":"8","author":"Y Bengio","year":"1997","unstructured":"Bengio, Y.: Using a financial training criterion rather than a prediction criterion. Int. J. Neural Syst. 8(04), 433\u2013443 (1997)","journal-title":"Int. J. Neural Syst."},{"key":"24_CR3","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.artint.2018.08.004","volume":"265","author":"L Chen","year":"2018","unstructured":"Chen, L., Feng, Y., Huang, S., Luo, B., Zhao, D.: Encoding implicit relation requirements for relation extraction: a joint inference approach. Artif. Intell. 265, 45\u201366 (2018)","journal-title":"Artif. Intell."},{"key":"24_CR4","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"24_CR5","unstructured":"Guns, T., Stuckey, P.J., Tack, G.: Solution dominance over constraint satisfaction problems. CoRR abs\/1812.09207 (2018)"},{"key":"24_CR6","unstructured":"Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks (2017)"},{"key":"24_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1007\/978-3-642-33558-7_68","volume-title":"Principles and Practice of Constraint Programming","author":"G Ifrim","year":"2012","unstructured":"Ifrim, G., O\u2019Sullivan, B., Simonis, H.: Properties of energy-price forecasts for scheduling. In: Milano, M. (ed.) CP 2012. LNCS, pp. 957\u2013972. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33558-7_68"},{"key":"24_CR8","unstructured":"Kool, W., van Hoof, H., Welling, M.: Attention, learn to solve routing problems! In: ICLR 2019: 7th International Conference on Learning Representations (2019)"},{"key":"24_CR9","doi-asserted-by":"crossref","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 (2017)","DOI":"10.1017\/S0140525X16001837"},{"issue":"11","key":"24_CR10","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278\u20132324 (1998)","journal-title":"Proc. IEEE"},{"key":"24_CR11","doi-asserted-by":"crossref","unstructured":"Li, Q., Anzaroot, S., Lin, W.P., Li, X., Ji, H.: Joint inference for cross-document information extraction. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, pp. 2225\u20132228. ACM (2011)","DOI":"10.1145\/2063576.2063932"},{"key":"24_CR12","unstructured":"Li, Q., Ji, H., Huang, L.: Joint event extraction via structured prediction with global features. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 73\u201382 (2013)"},{"key":"24_CR13","doi-asserted-by":"crossref","unstructured":"Li, T., Gupta, V., Mehta, M., Srikumar, V.: A logic-driven framework for consistency of neural models. arXiv preprint arXiv:1909.00126 (2019)","DOI":"10.18653\/v1\/D19-1405"},{"key":"24_CR14","unstructured":"Mandi, J., Demirovi\u0107, E., Stuckey, P., Guns, T., et al.: Smart predict-and-optimize for hard combinatorial optimization problems. arXiv preprint arXiv:1911.10092 (2019)"},{"key":"24_CR15","unstructured":"Manhaeve, R., Dumancic, S., Kimmig, A., Demeester, T., De Raedt, L.: Deepproblog: neural probabilistic logic programming. In: Advances in Neural Information Processing Systems, pp. 3749\u20133759 (2018)"},{"key":"24_CR16","unstructured":"M\u00e1rquez-Neila, P., Salzmann, M., Fua, P.: Imposing hard constraints on deep networks: promises and limitations. arXiv preprint arXiv:1706.02025 (2017)"},{"key":"24_CR17","unstructured":"Mukhopadhyay, A., Vorobeychik, Y., Dubey, A., Biswas, G.: Prioritized allocation of emergency responders based on a continuous-time incident prediction model. In: Adaptive Agents and Multi Agents Systems, pp. 168\u2013177 (2017)"},{"key":"24_CR18","unstructured":"van den Oord, A., Vinyals, O., et al.: Neural discrete representation learning. In: Advances in Neural Information Processing Systems, pp. 6306\u20136315 (2017)"},{"key":"24_CR19","unstructured":"Paszke, A., et al.: Automatic differentiation in PyTorch. In: NeurIPS Autodiff Workshop (2017)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Darrell, T.: Constrained convolutional neural networks for weakly supervised segmentation. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1796\u20131804 (2015)","DOI":"10.1109\/ICCV.2015.209"},{"key":"24_CR21","unstructured":"Perron, L., team: Google\u2019s or-tools"},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Platt, J.C.: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods. In: Advances in Large Margin Classifiers, pp. 61\u201374. MIT Press (1999)","DOI":"10.7551\/mitpress\/1113.003.0008"},{"key":"24_CR23","unstructured":"Platt, J.C., Barr, A.H.: Constrained differential optimization. In: Neural Information Processing Systems, pp. 612\u2013621 (1988)"},{"key":"24_CR24","unstructured":"Poon, H., Domingos, P.: Joint inference in information extraction. In: AAAI, vol. 7, pp. 913\u2013918 (2007)"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Punyakanok, V., Roth, D., Yih, W.T., Zimak, D.: Semantic role labeling via integer linear programming inference. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING 2004, p. 1346. Association for Computational Linguistics, USA (2004)","DOI":"10.3115\/1220355.1220552"},{"key":"24_CR26","unstructured":"Riedel, S.: Improving the accuracy and efficiency of map inference for Markov logic. arXiv preprint arXiv:1206.3282 (2012)"},{"key":"24_CR27","volume-title":"Handbook of Constraint Programming","author":"F Rossi","year":"2006","unstructured":"Rossi, F., Van Beek, P., Walsh, T.: Handbook of Constraint Programming. Elsevier, Amsterdam (2006)"},{"key":"24_CR28","unstructured":"The High-Level Expert Group on Artificial Intelligence (AI HLEG): A definition of AI (2017)"},{"issue":"1","key":"24_CR29","first-page":"1989","volume":"14","author":"T Tulabandhula","year":"2013","unstructured":"Tulabandhula, T., Rudin, C.: Machine learning with operational costs. J. Mach. Learn. Res. 14(1), 1989\u20132028 (2013)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR30","unstructured":"Wang, P.W., Donti, P., Wilder, B., Kolter, Z.: Satnet: bridging deep learning and logical reasoning using a differentiable satisfiability solver. In: ICML 2019: Thirty-Sixth International Conference on Machine Learning, pp. 6545\u20136554 (2019)"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Wang, Y., Chen, Q., Ahmed, M., Li, Z., Pan, W., Liu, H.: Joint inference for aspect-level sentiment analysis by deep neural networks and linguistic hints. IEEE Trans. Knowl. Data Eng. (2019)","DOI":"10.1109\/TKDE.2019.2947587"},{"key":"24_CR32","doi-asserted-by":"crossref","unstructured":"Wilder, B.: Melding the data-decisions pipeline: decision-focused learning for combinatorial optimization. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (2019)","DOI":"10.1609\/aaai.v33i01.33011658"}],"container-title":["Lecture Notes in Computer Science","Integration of Constraint Programming, Artificial Intelligence, and Operations Research"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58942-4_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T02:42:15Z","timestamp":1723603335000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-58942-4_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030589417","9783030589424"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58942-4_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"19 September 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CPAIOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vienna","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Austria","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cpaior2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cpaior2020.dbai.tuwien.ac.at\/","order":11,"name":"conference_url","label":"Conference URL","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":"72","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":"25","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":"7","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":"35% - 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":"3.08","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":"3.08","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)"}}]}}