{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T00:58:35Z","timestamp":1740099515574,"version":"3.37.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304867"},{"type":"electronic","value":"9783030304874"}],"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\/978-3-030-30487-4_22","type":"book-chapter","created":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:02:47Z","timestamp":1567969367000},"page":"281-295","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Capsule Generative Models"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4873-6928","authenticated-orcid":false,"given":"Yifeng","family":"Li","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3856-3696","authenticated-orcid":false,"given":"Xiaodan","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"key":"22_CR1","first-page":"1137","volume":"2","author":"Y Bengio","year":"2003","unstructured":"Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C.: A neural probabilistic language model. J. Mach. Learn. Res. 2, 1137\u20131155 (2003)","journal-title":"J. Mach. Learn. Res."},{"key":"22_CR2","unstructured":"Bornschein, J., Bengio, Y.: Reweighted wake-sleep. In: International Conference on Learning Representations (2015)"},{"key":"22_CR3","unstructured":"Courville, A., Bergstra, J., Bengio, Y.: A spike and slab restricted Boltzmann machine. In: International Conference on Artificial Intelligence and Statistics, pp. 233\u2013241 (2011)"},{"key":"22_CR4","doi-asserted-by":"publisher","first-page":"1022","DOI":"10.1162\/neco.1995.7.5.889","volume":"7","author":"P Dayan","year":"1995","unstructured":"Dayan, P., Hinton, G., Neal, R., Zemel, R.: The Helmholtz machine. Neural Comput. 7, 1022\u20131037 (1995). \n                      https:\/\/doi.org\/10.1162\/neco.1995.7.5.889","journal-title":"Neural Comput."},{"issue":"1\u20132","key":"22_CR5","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0010-0277(88)90031-5","volume":"28","author":"J Fodor","year":"1988","unstructured":"Fodor, J., Pylyshyn, Z.: Connectionism and cognitive architecture: a critical analysis. Cognition 28(1\u20132), 3\u201371 (1988). \n                      https:\/\/doi.org\/10.1016\/0010-0277(88)90031-5","journal-title":"Cognition"},{"key":"22_CR6","unstructured":"Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672\u20132680 (2014)"},{"key":"22_CR7","unstructured":"Hinton, G.: Aetherial symbols. In: AAAI Spring Symposium on Knowledge Representation and Reasoning: Integrating Symbolic and Neural Approaches (2015)"},{"issue":"6","key":"22_CR8","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MSP.2012.2205597","volume":"29","author":"G Hinton","year":"2012","unstructured":"Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29(6), 82\u201397 (2012). \n                      https:\/\/doi.org\/10.1109\/MSP.2012.2205597","journal-title":"IEEE Signal Process. Mag."},{"key":"22_CR9","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1007\/978-3-642-21735-7_6","volume-title":"Lecture Notes in Computer Science","author":"Geoffrey E. Hinton","year":"2011","unstructured":"Hinton, G., Krizhevsky, A., Wang, S.: Transforming auto-encoder. In: International Conference on Artificial Neural Networks, pp. 44\u201351 (2011). \n                      https:\/\/doi.org\/10.1007\/978-3-642-21735-7_6"},{"key":"22_CR10","first-page":"77","volume-title":"Parallel Distributed Processing: Explorations in the Microstructure of Cognition","author":"G Hinton","year":"1986","unstructured":"Hinton, G., McClelland, J., Rumelhart, D.: Distributed representations. In: Rumelhart, D., McClelland, J. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 77\u2013109. MIT Press, Cambridge (1986)"},{"key":"22_CR11","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"G Hinton","year":"2006","unstructured":"Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527\u20131554 (2006). \n                      https:\/\/doi.org\/10.1162\/neco.2006.18.7.1527","journal-title":"Neural Comput."},{"key":"22_CR12","unstructured":"Hinton, G., Sabour, S., Frosst, N.: Matrix capsules with EM routing. In: International Conference on Learning Representations (2018)"},{"key":"22_CR13","unstructured":"Kingma, D., Welling, M.: Auto-encoding variational Bayes. In: International Conference on Learning Representations (2014)"},{"key":"22_CR14","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097\u20131105 (2012)"},{"issue":"4","key":"22_CR15","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989). \n                      https:\/\/doi.org\/10.1162\/neco.1989.1.4.541","journal-title":"Neural Comput."},{"key":"22_CR16","unstructured":"Li, Y., Zhu, X.: Exploring Helmholtz machine and deep belief net in the exponential family perspective. In: ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models (2018)"},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Li, Y., Zhu, X.: Exponential family restricted Boltzmann machines and annealed importance sampling. In: International Joint Conference on Neural Networks, pp. 39\u201348 (2018). \n                      https:\/\/doi.org\/10.1109\/IJCNN.2018.8489413","DOI":"10.1109\/IJCNN.2018.8489413"},{"key":"22_CR18","unstructured":"Mnih, A., Gregor, K.: Neural variational inference and learning in belief networks. In: International Conference on Machine Learning, pp. II-1791\u2013II-1799 (2014)"},{"key":"22_CR19","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: International Conference on Machine Learning, pp. II-1278\u2013II-1286 (2014)"},{"key":"22_CR20","unstructured":"Sabour, S., Frosst, N., Hinton, G.: Dynamic routing between capsules. In: Neural Information Processing Systems, pp. 3856\u20133866 (2017)"},{"key":"22_CR21","unstructured":"Salakhutdinov, R.: Learning and evaluating Boltzmann machines. Department of Computer Science, University of Toronto, Toronto, Canada, Technical report (2008)"},{"key":"22_CR22","doi-asserted-by":"publisher","unstructured":"Tieleman, T.: Training restricted Boltzmann machines using approximations to the likelihood gradient. In: International Conference on Machine Learning, pp. 1064\u20131071 (2008). \n                      https:\/\/doi.org\/10.1145\/1390156.1390290","DOI":"10.1145\/1390156.1390290"},{"key":"22_CR23","unstructured":"Welling, M., Rosen-Zvi, M., Hinton, G.: Exponential family harmoniums with an application to information retrieval. In: Advances in Neural Information Processing Systems, pp. 1481\u20131488 (2005)"},{"key":"22_CR24","unstructured":"Xiao, H., Rasul, K., Vollgraf, R.: Fashion-MNIST: a novel image dataset for benchmarking machine learning algorithms. ArXiv p. \n                      arXiv:1708.07747v2\n                      \n                     (2017)"},{"key":"22_CR25","unstructured":"Zhao, S., Song, J., Ermon, S.: Towards a deeper understanding of variational autoencoding models. arXiv p. \n                      arXiv:1702.08658\n                      \n                     (2017)"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Theoretical Neural Computation"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30487-4_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,8]],"date-time":"2019-09-08T19:13:28Z","timestamp":1567970008000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-30487-4_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304867","9783030304874"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30487-4_22","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":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","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":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}