{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,11]],"date-time":"2024-09-11T18:42:48Z","timestamp":1726080168105},"publisher-location":"Cham","reference-count":23,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030454388"},{"type":"electronic","value":"9783030454395"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-45439-5_53","type":"book-chapter","created":{"date-parts":[[2020,4,11]],"date-time":"2020-04-11T04:02:50Z","timestamp":1586577770000},"page":"807-820","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views"],"prefix":"10.1007","author":[{"given":"Anastasiia","family":"Doinychko","sequence":"first","affiliation":[]},{"given":"Massih-Reza","family":"Amini","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,8]]},"reference":[{"issue":"1\u20132","key":"53_CR1","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s10994-009-5151-5","volume":"79","author":"MR Amini","year":"2010","unstructured":"Amini, M.R., Goutte, C.: A co-classification approach to learning from multilingual corpora. Mach. Learn. J. 79(1\u20132), 105\u2013121 (2010)","journal-title":"Mach. Learn. J."},{"key":"53_CR2","unstructured":"Amini, M.R., Usunier, N., Goutte, C.: Learning from multiple partially observed views - an application to multilingual text categorization. In: Advances in Neural Information Processing Systems, vol. 22, 28\u201336 (2009)"},{"issue":"Jul","key":"53_CR3","first-page":"1","volume":"3","author":"FR Bach","year":"2003","unstructured":"Bach, F.R., Jordan, M.I.: Kernel independent component analysis. J. Mach. Learn. Res. 3(Jul), 1\u201348 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"53_CR4","doi-asserted-by":"crossref","unstructured":"Bach, F.R., Lanckriet, G.R.G., Jordan, M.I.: Multiple kernel learning, conic duality, and the SMO algorithm. In: Proceedings of the $$21^{st}$$ International Conference on Machine Learning (ICML) (2004)","DOI":"10.1145\/1015330.1015424"},{"key":"53_CR5","doi-asserted-by":"crossref","unstructured":"Blum, A., Mitchell, T.: Combining labeled and unlabeled data with co-training. In: Proceedings of the Eleventh Annual Conference on Computational Learning Theory (COLT), pp. 92\u2013100 (1998)","DOI":"10.1145\/279943.279962"},{"key":"53_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"175","DOI":"10.1007\/978-3-319-71246-8_11","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"M Chen","year":"2017","unstructured":"Chen, M., Denoyer, L.: Multi-view generative adversarial networks. In: Ceci, M., Hollm\u00e9n, J., Todorovski, L., Vens, C., D\u017eeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10535, pp. 175\u2013188. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71246-8_11"},{"key":"53_CR7","unstructured":"Denton, E.L., Chintala, S., Szlam, A., Fergus, R.: Deep generative image models using a laplacian pyramid of adversarial networks. In: Advances in Neural Information Processing Systems, vol. 28, 1486\u20131494 (2015)"},{"key":"53_CR8","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., Darrell, T.: Adversarial feature learning. In: International Conference on Representation Learning (ICLR) (2017)"},{"key":"53_CR9","unstructured":"Dumoulin, V., et al.: Adversarially learned inference. In: International Conference on Representation Learning (ICLR) (2017)"},{"key":"53_CR10","unstructured":"Farquhar, J., Hardoon, D., Meng, H., Shawe-taylor, J.S., Szedm\u00e1k, S.: Two view learning: SVM-2K, theory and practice. In: Advances in Neural Information Processing Systems vol. 18, pp. 355\u2013362 (2006)"},{"key":"53_CR11","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27, pp. 2672\u20132680 (2014)"},{"key":"53_CR12","doi-asserted-by":"crossref","unstructured":"Goyal, A., Morvant, E., Germain, P., Amini, M.R.: PAC-Bayesian Analysis for a two-step Hierarchical Multiview Learning Approach. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 205\u2013221 (2017)","DOI":"10.1007\/978-3-319-71246-8_13"},{"key":"53_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference on Representation Learning (ICLR) (2015)"},{"key":"53_CR14","unstructured":"Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., Van Gool, L.: Pose guided person image generation. In: Advances in Neural Information Processing Systems, vol. 30, pp. 406\u2013416 (2017)"},{"key":"53_CR15","unstructured":"Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the 34th International Conference on Machine Learning (ICML), pp. 2642\u20132651 (2017)"},{"key":"53_CR16","unstructured":"Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Representation Learning (ICLR) (2016)"},{"key":"53_CR17","unstructured":"Salimans, T., et al.: Improved techniques for training GANs. In: Advances in Neural Information Processing Systems, vol. 29, pp. 2234\u20132242 (2016)"},{"key":"53_CR18","doi-asserted-by":"crossref","unstructured":"Sindhwani, V., Rosenberg, D.S.: An RKHS for multi-view learning and manifold co-regularization. In: Proceedings of the $$25^{th}$$ International Conference on Machine Learning (ICML) (2008)","DOI":"10.1145\/1390156.1390279"},{"key":"53_CR19","unstructured":"Springenberg, J.T.: Unsupervised and semi-supervised learning with categorical generative adversarial networks. In: International Conference on Representation Learning (ICLR) (2016)"},{"key":"53_CR20","unstructured":"Tian, L., Nie, F., Li, X.: A unified weight learning paradigm for multi-view learning. In: Proceedings of Machine Learning Research, pp. 2790\u20132800 (2019)"},{"key":"53_CR21","doi-asserted-by":"crossref","unstructured":"Tian, Y., Peng, X., Zhao, L., Zhang, S., Metaxas, D.N.: CR-GAN: learning complete representations for multi-view generation. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (IJCAI), pp. 942\u2013948 (2018)","DOI":"10.24963\/ijcai.2018\/131"},{"key":"53_CR22","doi-asserted-by":"crossref","unstructured":"Ueffing, N., Simard, M., Larkin, S., Johnson, H.: NRC\u2019s portage system for WMT 2007. In: Proceedings of the Second Workshop on Statistical Machine Translation, pp. 185\u2013188 (2007)","DOI":"10.3115\/1626355.1626379"},{"key":"53_CR23","doi-asserted-by":"crossref","unstructured":"Zhao, B., Wu, X., Cheng, Z., Liu, H., Feng, J.: Multi-view image generation from a single-view. In: Proceedings of the 26th ACM International Conference on Multimedia (MM), pp. 383\u2013391 (2018)","DOI":"10.1145\/3240508.3240536"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-45439-5_53","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T19:21:58Z","timestamp":1710357718000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-45439-5_53"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030454388","9783030454395"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-45439-5_53","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":"8 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lisbon","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","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":"14 April 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 April 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":"ecir2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2020.org\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"457","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":"55","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":"46","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":"12% - 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":"4","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","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)"}},{"value":"Also included: 8 reproducibility papers, 10 demonstration papers, 12 CLEF organizers lab track papers, 7 doctoral consortium papers, 4 workshops, 3 tutorials. Due to the COVID-19 pandemic, this conference was held virtually.","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)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}