{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T05:39:56Z","timestamp":1726033196204},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030223670"},{"type":"electronic","value":"9783030223687"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","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":[[2019]]},"DOI":"10.1007\/978-3-030-22368-7_31","type":"book-chapter","created":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T23:08:07Z","timestamp":1561072087000},"page":"393-405","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Learning Adaptive Regularization for Image Labeling Using Geometric Assignment"],"prefix":"10.1007","author":[{"given":"Ruben","family":"H\u00fchnerbein","sequence":"first","affiliation":[]},{"given":"Fabrizio","family":"Savarino","sequence":"additional","affiliation":[]},{"given":"Stefania","family":"Petra","sequence":"additional","affiliation":[]},{"given":"Christoph","family":"Schn\u00f6rr","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,6,5]]},"reference":[{"key":"31_CR1","volume-title":"Methods of Information Geometry","author":"SI Amari","year":"2000","unstructured":"Amari, S.I., Nagaoka, H.: Methods of Information Geometry. American. Mathematical Society, Oxford University Press, Oxford (2000)"},{"issue":"2","key":"31_CR2","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1007\/s10851-016-0702-4","volume":"58","author":"F Astr\u00f6m","year":"2017","unstructured":"Astr\u00f6m, F., Petra, S., Schmitzer, B., Schn\u00f6rr, C.: Image labeling by assignment. J. Math. Imaging Vis. 58(2), 211\u2013238 (2017)","journal-title":"J. Math. Imaging Vis."},{"key":"31_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/978-3-319-58771-4_30","volume-title":"Scale Space and Variational Methods in Computer Vision","author":"F \u00c5str\u00f6m","year":"2017","unstructured":"\u00c5str\u00f6m, F., H\u00fchnerbein, R., Savarino, F., Recknagel, J., Schn\u00f6rr, C.: MAP image labeling using Wasserstein messages and geometric assignment. In: Lauze, F., Dong, Y., Dahl, A.B. (eds.) SSVM 2017. LNCS, vol. 10302, pp. 373\u2013385. Springer, Cham (2017). \n                      https:\/\/doi.org\/10.1007\/978-3-319-58771-4_30"},{"issue":"1","key":"31_CR4","first-page":"1","volume":"5","author":"E Weinan","year":"2017","unstructured":"Weinan, E.: A proposal on machine learning via dynamical systems. Commun. Math. Stat. 5(1), 1\u201311 (2017)","journal-title":"Commun. Math. Stat."},{"issue":"3","key":"31_CR5","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1023\/A:1011430410075","volume":"65","author":"MB Giles","year":"2000","unstructured":"Giles, M.B., Pierce, N.A.: An introduction to the adjoint approach to design. Flow Turbul. Combust. 65(3), 393\u2013415 (2000)","journal-title":"Flow Turbul. Combust."},{"key":"31_CR6","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1017\/S0962492902000132","volume":"12","author":"A Griewank","year":"2003","unstructured":"Griewank, A.: A mathematical view of automatic differentiation. Acta Numerica 12, 321\u2013398 (2003)","journal-title":"Acta Numerica"},{"issue":"1","key":"31_CR7","doi-asserted-by":"publisher","first-page":"014004","DOI":"10.1088\/1361-6420\/aa9a90","volume":"34","author":"E Haber","year":"2017","unstructured":"Haber, E., Ruthotto, L.: Stable architectures for deep neural networks. Inverse Prob. 34(1), 014004 (2017)","journal-title":"Inverse Prob."},{"key":"31_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-30666-8","volume-title":"Geometric Numerical Integration","author":"E Hairer","year":"2006","unstructured":"Hairer, E., Lubich, C., Wanner, G.: Geometric Numerical Integration. Springer, Heidelberg (2006). \n                      https:\/\/doi.org\/10.1007\/3-540-30666-8"},{"key":"31_CR9","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"issue":"2","key":"31_CR10","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1007\/s11263-015-0809-x","volume":"115","author":"J Kappes","year":"2015","unstructured":"Kappes, J., et al.: A comparative study of modern inference techniques for structured discrete energy minimization problems. Int. J. Comput. Vis. 115(2), 155\u2013184 (2015)","journal-title":"Int. J. Comput. Vis."},{"key":"31_CR11","unstructured":"Phillips, J.: Coresets and sketches. In: Handbook of Discrete and Computational Geometry. CRC Press (2016). Chapter 48"},{"issue":"1","key":"31_CR12","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1196\/annals.1370.015","volume":"1065","author":"I Ross","year":"2005","unstructured":"Ross, I.: A roadmap for optimal control: the right way to commute. Ann. N. Y. Acad. Sci. 1065(1), 210\u2013231 (2005)","journal-title":"Ann. N. Y. Acad. Sci."},{"key":"31_CR13","unstructured":"Shalev-Shwartz, S., Shamir, O., Shammah, S.: Failures of gradient-based deep learning. CoRR abs\/1703.07950 (2017)"},{"key":"31_CR14","first-page":"1829","volume":"7","author":"MJ Wainwright","year":"2006","unstructured":"Wainwright, M.J.: Estimating the \u201cWrong\u201d graphical model: benefits in the computation-limited setting. J. Mach. Learn. Res. 7, 1829\u20131859 (2006)","journal-title":"J. Mach. Learn. Res."},{"key":"31_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-30623-4","volume-title":"All of Nonparametric Statistics","author":"L Wasserman","year":"2006","unstructured":"Wasserman, L.: All of Nonparametric Statistics. Springer, New York (2006). \n                      https:\/\/doi.org\/10.1007\/0-387-30623-4"},{"key":"31_CR16","doi-asserted-by":"crossref","unstructured":"Zeilmann, A., Savarino, F., Petra, S., Schn\u00f6rr, C.: Geometric numerical integration of the assignment flow. CoRR abs\/1810.06970 (2018)","DOI":"10.1088\/1361-6420\/ab2772"},{"issue":"7","key":"31_CR17","doi-asserted-by":"publisher","first-page":"1001","DOI":"10.1109\/TPAMI.2002.1017626","volume":"24","author":"SC Zhu","year":"2002","unstructured":"Zhu, S.C., Liu, X.: Learning in Gibbsian fields: how accurate and how fast can it be? IEEE Trans. Patt. Anal. Mach. Intell. 24(7), 1001\u20131006 (2002)","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."}],"container-title":["Lecture Notes in Computer Science","Scale Space and Variational Methods in Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-22368-7_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,6,20]],"date-time":"2019-06-20T23:10:24Z","timestamp":1561072224000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-22368-7_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030223670","9783030223687"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-22368-7_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"5 June 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"SSVM","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Scale Space and Variational Methods in Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hofgeismar","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 June 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 July 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"scalespace2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ssvm2019.mic.uni-luebeck.de\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}