{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T20:30:32Z","timestamp":1757622632474,"version":"3.44.0"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030865191"},{"type":"electronic","value":"9783030865207"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86520-7_7","type":"book-chapter","created":{"date-parts":[[2021,9,9]],"date-time":"2021-09-09T15:25:48Z","timestamp":1631201148000},"page":"100-115","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Variational Hyper-encoding Networks"],"prefix":"10.1007","author":[{"given":"Phuoc","family":"Nguyen","sequence":"first","affiliation":[]},{"given":"Truyen","family":"Tran","sequence":"additional","affiliation":[]},{"given":"Sunil","family":"Gupta","sequence":"additional","affiliation":[]},{"given":"Santu","family":"Rana","sequence":"additional","affiliation":[]},{"given":"Hieu-Chi","family":"Dam","sequence":"additional","affiliation":[]},{"given":"Svetha","family":"Venkatesh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,10]]},"reference":[{"key":"7_CR1","unstructured":"Chen, X., et al.: Variational lossy autoencoder. arXiv preprint arXiv:1611.02731 (2016)"},{"key":"7_CR2","unstructured":"Choi, K., Wu, M., Goodman, N., Ermon, S.: Meta-amortized variational inference and learning. arXiv preprint arXiv:1902.01950 (2019)"},{"key":"7_CR3","unstructured":"Do, K., Tran, T., Venkatesh, S.: Matrix-centric neural networks. arXiv preprint arXiv:1703.01454 (2017)"},{"key":"7_CR4","unstructured":"Do, K., Tran, T., Venkatesh, S.: Learning deep matrix representations. arXiv preprint arXiv:1703.01454 (2018)"},{"key":"7_CR5","unstructured":"Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 1126\u20131135 (2017). JMLR.org"},{"key":"7_CR6","unstructured":"Finn, C., Levine, S.: Meta-learning and universality: deep representations and gradient descent can approximate any learning algorithm. In: ICLR (2018)"},{"issue":"2","key":"7_CR7","doi-asserted-by":"publisher","first-page":"268","DOI":"10.1021\/acscentsci.7b00572","volume":"4","author":"R G\u00f3mez-Bombarelli","year":"2018","unstructured":"G\u00f3mez-Bombarelli, R., et al.: Automatic chemical design using a data-driven continuous representation of molecules. ACS Central Sci. 4(2), 268\u2013276 (2018)","journal-title":"ACS Central Sci."},{"key":"7_CR8","unstructured":"Grant, E., Finn, C., Levine, S., Darrell, T., Griffiths, T.: Recasting gradient-based meta-learning as hierarchical Byes. arXiv preprint arXiv:1801.08930 (2018)"},{"key":"7_CR9","unstructured":"Ha, D., Dai, A., Le, Q.V.: Hypernetworks. arXiv preprint arXiv:1609.09106 (2016)"},{"key":"7_CR10","doi-asserted-by":"crossref","unstructured":"Hinton, G., Van Camp, D.: Keeping neural networks simple by minimizing the description length of the weights. In: Proceedings of the 6th Annual ACM Conference on Computational Learning Theory. Citeseer (1993)","DOI":"10.1145\/168304.168306"},{"key":"7_CR11","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)"},{"key":"7_CR12","unstructured":"Krueger, D., Huang, C.-W., Islam, R., Turner, R., Lacoste, A., Courville, A.: Bayesian hypernetworks. arXiv preprint arXiv:1710.04759 (2017)"},{"key":"7_CR13","unstructured":"Le, H., Tran, T., Nguyen, T., Venkatesh, S.: Variational memory encoder-decoder. In: NeurIPS (2018)"},{"key":"7_CR14","unstructured":"Mishra, N., Rohaninejad, M., Chen, X., Abbeel, P.: A simple neural attentive meta-learner. In: ICLR 2018 (2018)"},{"key":"7_CR15","unstructured":"Nguyen, C., Li, Y., Bui, T.D., Turner, R.E.: Variational continual learning. In: ICLR (2018)"},{"key":"7_CR16","doi-asserted-by":"crossref","unstructured":"Nguyen, P., Tran, T., Gupta, S., Rana, S., Barnett, M., Venkatesh, S.: Incomplete conditional density estimation for fast materials discovery. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 549\u2013557. SIAM (2019)","DOI":"10.1137\/1.9781611975673.62"},{"key":"7_CR17","unstructured":"Rao, D., Visin, F., Rusu, A., Pascanu, R., Teh, Y.W., Hadsell, R.: Continual unsupervised representation learning. In: Advances in Neural Information Processing Systems, pp. 7645\u20137655 (2019)"},{"key":"7_CR18","unstructured":"Ratzlaff, N., Fuxin, L.: HyperGAN: a generative model for diverse. In: Performant Neural Networks, ICML (2019)"},{"key":"7_CR19","unstructured":"Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. arXiv preprint arXiv:1401.4082 (2014)"},{"issue":"1","key":"7_CR20","doi-asserted-by":"publisher","first-page":"148","DOI":"10.1109\/JPROC.2015.2494218","volume":"104","author":"B Shahriari","year":"2016","unstructured":"Shahriari, B., Swersky, K., Wang, Z., Adams, R.P., De Freitas, N.: Taking the human out of the loop: a review of Bayesian optimization. Proc. IEEE 104(1), 148\u2013175 (2016)","journal-title":"Proc. IEEE"},{"key":"7_CR21","unstructured":"Tomczak, J., Welling, M.: VAE with a VampPrior. arXiv preprint arXiv:1705.07120 (2017)"},{"key":"7_CR22","unstructured":"Townsend, J., Bird, T., Barber, D.: Practical lossless compression with latent variables using bits back coding. arXiv preprint arXiv:1901.04866 (2019)"},{"key":"7_CR23","unstructured":"Wang, K.-C., Vicol, P., Lucas, J., Gu, L., Grosse, R., Zemel, R.: Adversarial distillation of Bayesian neural network posteriors. In: International Conference on Machine Learning, pp. 5177\u20135186 (2018)"},{"key":"7_CR24","unstructured":"Yoon, J., Kim, T., Dia, O., Kim, S., Bengio, Y., Ahn, S.: Bayesian model-agnostic meta-learning. In: Advances in Neural Information Processing Systems, pp. 7332\u20137342 (2018)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases. Research Track"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86520-7_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T22:02:24Z","timestamp":1757368944000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86520-7_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030865191","9783030865207"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86520-7_7","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":"10 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bilbao","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2021.ecmlpkdd.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":"869","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":"210","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":"24% - 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":"3-4","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":"3-9","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":"The conference was held online 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)"}}]}}