{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T17:35:46Z","timestamp":1743096946437,"version":"3.40.3"},"publisher-location":"Cham","reference-count":17,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030712778"},{"type":"electronic","value":"9783030712785"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-71278-5_1","type":"book-chapter","created":{"date-parts":[[2021,3,16]],"date-time":"2021-03-16T08:05:38Z","timestamp":1615881938000},"page":"1-14","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Characterizing the Role of a Single Coupling Layer in Affine Normalizing Flows"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0978-1539","authenticated-orcid":false,"given":"Felix","family":"Draxler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1825-3826","authenticated-orcid":false,"given":"Jonathan","family":"Schwarz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8999-2338","authenticated-orcid":false,"given":"Christoph","family":"Schn\u00f6rr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6036-1287","authenticated-orcid":false,"given":"Ullrich","family":"K\u00f6the","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,17]]},"reference":[{"key":"1_CR1","unstructured":"Ardizzone, L., Kruse, J., Rother, C., K\u00f6the, U.: Analyzing inverse problems with invertible neural networks. In: International Conference on Learning Representations (2018)"},{"key":"1_CR2","unstructured":"Bigoni, D., Zahm, O., Spantini, A., Marzouk, Y.: Greedy inference with layers of lazy maps. arXiv preprint arXiv:1906.00031 (2019)"},{"key":"1_CR3","unstructured":"Dinh, L., Krueger, D., Bengio, Y.: NICE: non-linear independent components estimation. arXiv preprint arXiv:1410.8516 (2014)"},{"key":"1_CR4","unstructured":"Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real nvp. arXiv preprint arXiv:1605.08803 (2016)"},{"issue":"4","key":"1_CR5","doi-asserted-by":"publisher","first-page":"521","DOI":"10.1007\/BF02293811","volume":"43","author":"AI Fleishman","year":"1978","unstructured":"Fleishman, A.I.: A method for simulating non-normal distributions. Psychometrika 43(4), 521\u2013532 (1978)","journal-title":"Psychometrika"},{"key":"1_CR6","unstructured":"Hoogeboom, E., Peters, J., van den Berg, R., Welling, M.: Integer discrete flows and lossless compression. In: Advances in Neural Information Processing Systems, pp. 12134\u201312144 (2019)"},{"key":"1_CR7","unstructured":"Jacobsen, J.H., Behrmann, J., Zemel, R., Bethge, M.: Excessive invariance causes adversarial vulnerability. arXiv preprint arXiv:1811.00401 (2018)"},{"key":"1_CR8","unstructured":"Jaini, P., Selby, K.A., Yu, Y.: Sum-of-squares polynomial flow. arXiv preprint arXiv:1905.02325 (2019)"},{"key":"1_CR9","unstructured":"Kingma, D.P., Dhariwal, P.: Glow: generative flow with invertible 1x1 convolutions. In: Advances in Neural Information Processing Systems, pp. 10215\u201310224 (2018)"},{"key":"1_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-319-11259-6_23-1","volume-title":"Handbook of Uncertainty Quantification","author":"Y Marzouk","year":"2016","unstructured":"Marzouk, Y., Moselhy, T., Parno, M., Spantini, A.: Sampling via measure transport: an introduction. In: Ghanem, R., Higdon, D., Owhadi, H. (eds.) Handbook of Uncertainty Quantification, pp. 1\u201341. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-11259-6_23-1"},{"key":"1_CR11","unstructured":"Meng, C., Ke, Y., Zhang, J., Zhang, M., Zhong, W., Ma, P.: Large-scale optimal transport map estimation using projection pursuit. 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. 8116\u20138127. Curran Associates, Inc. (2019)"},{"key":"1_CR12","unstructured":"Nalisnick, E., Matsukawa, A., Teh, Y.W., Lakshminarayanan, B.: Detecting out-of-distribution inputs to deep generative models using a test for typicality. arXiv preprint arXiv:1906.02994 (2019)"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"No\u00e9, F., Olsson, S., K\u00f6hler, J., Wu, H.: Boltzmann generators: sampling equilibrium states of many-body systems with deep learning. Science 365(6457), eaaw1147 (2019)","DOI":"10.1126\/science.aaw1147"},{"key":"1_CR14","unstructured":"Papamakarios, G., Nalisnick, E., Rezende, D.J., Mohamed, S., Lakshminarayanan, B.: Normalizing flows for probabilistic modeling and inference. arXiv preprint arXiv:1912.02762 (2019)"},{"key":"1_CR15","unstructured":"Putzky, P., Welling, M.: Invert to learn to invert. In: Advances in Neural Information Processing Systems, pp. 446\u2013456 (2019)"},{"issue":"4","key":"1_CR16","first-page":"727","volume":"7","author":"EG Tabak","year":"2018","unstructured":"Tabak, E.G., Trigila, G.: Conditional expectation estimation through attributable components. Inf. Infer. J. IMA 7(4), 727\u2013754 (2018)","journal-title":"Inf. Infer. J. IMA"},{"issue":"4","key":"1_CR17","doi-asserted-by":"publisher","first-page":"613","DOI":"10.1002\/cpa.21588","volume":"69","author":"G Trigila","year":"2016","unstructured":"Trigila, G., Tabak, E.G.: Data-driven optimal transport. Commun. Pure Appl. Math. 69(4), 613\u2013648 (2016)","journal-title":"Commun. Pure Appl. Math."}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-71278-5_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,23]],"date-time":"2021-04-23T10:08:06Z","timestamp":1619172486000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-71278-5_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030712778","9783030712785"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-71278-5_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"17 March 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"T\u00fcbingen","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 September 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"42","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.gcpr-vmv-vcbm-2020.uni-tuebingen.de\/?page_id=102","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT3","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"89","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":"35","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":"39% - 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","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":"4.15","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)"}},{"value":"The conference took place virtually due to the COVID-19 pandemic","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}