{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T13:21:23Z","timestamp":1788960083096,"version":"build-2803163510"},"reference-count":28,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,12,17]],"date-time":"2022-12-17T00:00:00Z","timestamp":1671235200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007756","name":"University of Arkansas","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007756","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,12,17]]},"DOI":"10.1109\/bigdata55660.2022.10021114","type":"proceedings-article","created":{"date-parts":[[2023,1,26]],"date-time":"2023-01-26T19:35:23Z","timestamp":1674761723000},"page":"1014-1023","source":"Crossref","is-referenced-by-count":10,"title":["SCM-VAE: Learning Identifiable Causal Representations via Structural Knowledge"],"prefix":"10.1109","author":[{"given":"Aneesh","family":"Komanduri","sequence":"first","affiliation":[{"name":"University of Arkansas,Fayetteville,Arkansas,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongkai","family":"Wu","sequence":"additional","affiliation":[{"name":"Clemson University,Clemson,South Carolina,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wen","family":"Huang","sequence":"additional","affiliation":[{"name":"University of Arkansas,Fayetteville,Arkansas,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Texas at Dallas,Richardson,Texas,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xintao","family":"Wu","sequence":"additional","affiliation":[{"name":"University of Arkansas,Fayetteville,Arkansas,USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9780511803161","volume-title":"Causality","author":"Pearl","year":"2009"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1145\/3527154"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2013.50"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2021.3058954"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00947"},{"key":"ref6","article-title":"CITRIS: Causal identifiability from temporal intervened sequences","volume-title":"Proceedings of the 39th International Conference on Machine Learning","author":"Lippe"},{"key":"ref7","article-title":"Invariant causal representation learning for out-of-distribution generalization","volume-title":"International Conference on Learning Representations","author":"Lu"},{"key":"ref8","article-title":"Variational autoencoders and nonlinear ica: A unifying framework","volume-title":"Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics","author":"Khemakhem"},{"key":"ref9","article-title":"Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA","volume-title":"First Conference on Causal Learning and Reasoning","author":"Lachapelle"},{"key":"ref10","article-title":"Weakly-supervised disentanglement without compromises","volume-title":"Proceedings of the 37th International Conference on Machine Learning","author":"Locatello"},{"key":"ref11","article-title":"The causal-neural connection: Expressiveness, learnability, and inference","author":"Xia","year":"2021","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref12","article-title":"Challenging common assumptions in the unsupervised learning of disentangled representations","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Locatello"},{"issue":"241","key":"ref13","first-page":"1","article-title":"Weakly supervised disentangled generative causal representation learning","volume":"23","author":"Shen","year":"2022","journal-title":"Journal of Machine Learning Research"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1137\/1.9781611977172.48"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-33374-3_37"},{"key":"ref16","article-title":"Learning structured output representation using deep conditional generative models","author":"Sohn","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref17","article-title":"beta-VAE: Learning basic visual concepts with a constrained variational framework","volume-title":"International Conference on Learning Representations","author":"Higgins"},{"key":"ref18","article-title":"Learning independent features with adversarial nets for non-linear ica","author":"Brakel","year":"2017"},{"key":"ref19","article-title":"Robustly disentangled causal mechanisms: Validating deep representations for interventional robustness","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Suter"},{"key":"ref20","article-title":"Causalgan: Learning causal implicit generative models with adversarial training","author":"Kocaoglu","year":"2017"},{"key":"ref21","article-title":"Dag-gnn: Dag structure learning with graph neural networks","volume-title":"Proceedings of the 36th International Conference on Machine Learning","author":"Yu"},{"key":"ref22","article-title":"Weakly supervised causal representation learning","volume-title":"ICLR2022 Workshop on the Elements of Reasoning: Objects, Structure and Causality","author":"Brehmer"},{"key":"ref23","article-title":"Dags with no tears: Continuous optimization for structure learning","author":"Zheng","year":"2018","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref24","volume-title":"Elements of Causal Inference: Foundations and Learning Algorithms","author":"Peters","year":"2017"},{"key":"ref25","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1145\/3501714.3501743"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.425"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1309933111"}],"event":{"name":"2022 IEEE International Conference on Big Data (Big Data)","location":"Osaka, Japan","start":{"date-parts":[[2022,12,17]]},"end":{"date-parts":[[2022,12,20]]}},"container-title":["2022 IEEE International Conference on Big Data (Big Data)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/10020192\/10020156\/10021114.pdf?arnumber=10021114","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T15:24:44Z","timestamp":1709393084000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10021114\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,17]]},"references-count":28,"URL":"https:\/\/doi.org\/10.1109\/bigdata55660.2022.10021114","relation":{},"subject":[],"published":{"date-parts":[[2022,12,17]]}}}