{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T06:36:19Z","timestamp":1743057379160,"version":"3.40.3"},"publisher-location":"Cham","reference-count":30,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031434204"},{"type":"electronic","value":"9783031434211"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-43421-1_41","type":"book-chapter","created":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T20:37:24Z","timestamp":1694983044000},"page":"693-708","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Equivariant Representation Learning in\u00a0the\u00a0Presence of\u00a0Stabilizers"],"prefix":"10.1007","author":[{"given":"Luis Armando","family":"P\u00e9rez Rey","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Giovanni Luca","family":"Marchetti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danica","family":"Kragic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dmitri","family":"Jarnikov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mike","family":"Holenderski","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,18]]},"reference":[{"key":"41_CR1","unstructured":"Ahuja, K., Hartford, J., Bengio, Y.: Properties from nechanisms: an equivariance perspective on identifiable representation learning. In: International Conference on Learning Representations (2022)"},{"issue":"8","key":"41_CR2","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798\u20131828 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"41_CR3","unstructured":"Caselles-Dupr\u00e9, H., Garcia-Ortiz, M., Filliat, D.: Symmetry-based disentangled representation learning requires interaction with environments. In: Advances in Neural Information Processing Systems (2019)"},{"key":"41_CR4","unstructured":"Chen, R.T., Li, X., Grosse, R.B., Duvenaud, D.K.: Isolating sources of disentanglement in variational autoencoders. In: Advances in Neural Information Processing Systems (2018)"},{"key":"41_CR5","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (2020)"},{"key":"41_CR6","unstructured":"Cohen, T., Welling, M.: Learning the irreducible representations of commutative Lie groups. In: International Conference on Machine Learning (2014)"},{"key":"41_CR7","unstructured":"Dupont, E., Martin, M.B., Colburn, A., Sankar, A., Susskind, J., Shan, Q.: Equivariant neural rendering. In: International Conference on Machine Learning (2020)"},{"key":"41_CR8","unstructured":"Falorsi, L., et al.: Explorations in homeomorphic variational auto-encoding. In: ICML18 Workshop on Theoretical Foundations and Applications of Deep Generative Models (2018)"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Guo, X., Zhu, E., Liu, X., Yin, J.: Affine equivariant autoencoder. In: International Joint Conference on Artificial Intelligence (2019)","DOI":"10.24963\/ijcai.2019\/335"},{"key":"41_CR10","unstructured":"Ha, D., Schmidhuber, J.: World models. arXiv preprint (2018)"},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"41_CR12","unstructured":"Higgins, I., et al.: Towards a definition of disentangled representations. arXiv preprint (2018)"},{"key":"41_CR13","unstructured":"Higgins, I., et al.: Beta-VAE: learning basic visual concepts with a constrained variational framework. In: International Conference on Learning Representations (2017)"},{"key":"41_CR14","doi-asserted-by":"publisher","DOI":"10.3389\/fncom.2022.836498","volume":"16","author":"I Higgins","year":"2022","unstructured":"Higgins, I., Racani\u00e8re, S., Rezende, D.: Symmetry-based representations for artificial and biological general intelligence. Front. Comput. Neurosci. 16, 836498 (2022)","journal-title":"Front. Comput. Neurosci."},{"key":"41_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1007\/978-3-642-21735-7_6","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2011","author":"GE Hinton","year":"2011","unstructured":"Hinton, G.E., Krizhevsky, A., Wang, S.D.: Transforming auto-encoders. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 44\u201351. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-21735-7_6"},{"key":"41_CR16","unstructured":"Kipf, T., van der Pol, E., Welling, M.: Contrastive learning of structured world models. In: International Conference on Learning Representations (2020)"},{"key":"41_CR17","unstructured":"Liang, K.K.: Efficient conversion from rotating matrix to rotation axis and angle by extending rodrigues\u2019 formula. arXiv preprint (2018)"},{"key":"41_CR18","unstructured":"Locatello, F., et al.: Challenging common assumptions in the unsupervised learning of disentangled representations. In: International Conference on Machine Learning (2019)"},{"key":"41_CR19","unstructured":"Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. In: International Conference on Learning Representations (2019)"},{"issue":"12","key":"41_CR20","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/TVCG.2015.2513408","volume":"22","author":"E Marchand","year":"2016","unstructured":"Marchand, E., Uchiyama, H., Spindler, F.: Pose estimation for augmented reality: a hands-on survey. IEEE Trans. Vis. Comput. Graph. 22(12), 2633\u20132651 (2016)","journal-title":"IEEE Trans. Vis. Comput. Graph."},{"key":"41_CR21","unstructured":"Marchetti, G.L., Tegn\u00e9r, G., Varava, A., Kragic, D.: Equivariant Representation Learning via Class-Pose Decomposition. arXiv preprint (2022)"},{"key":"41_CR22","unstructured":"Murphy, K., Esteves, C., Jampani, V., Ramalingam, S., Makadia, A.: Implicit representation of probability distributions on the rotation manifold. In: International Conference on Machine Learning (2021)"},{"key":"41_CR23","unstructured":"Painter, M., Hare, J., Pr\u00fcgel-Bennett, A.: Linear disentangled representations and unsupervised action estimation. In: Advances in Neural Information Processing Systems (2020)"},{"key":"41_CR24","unstructured":"Park, J.Y., Biza, O., Zhao, L., van de Meent, J.W., Walters, R.: Learning symmetric embeddings for equivariant world models. In: International Conference on Machine Learning (2022)"},{"key":"41_CR25","unstructured":"van der Pol, E., Kipf, T., Oliehoek, F.A., Welling, M.: Plannable approximations to MDP homomorphisms: equivariance under actions. In: International Conference on Autonomous Agents and Multi-Agent Systems (2020)"},{"key":"41_CR26","unstructured":"Quessard, R., Barrett, T.D., Clements, W.R.: Learning disentangled representations and group structure of dynamical environments. In: Advances in Neural Information Processing Systems (2020)"},{"key":"41_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4612-4176-8","volume-title":"An Introduction to the Theory of Groups","author":"JJ Rotman","year":"2012","unstructured":"Rotman, J.J.: An Introduction to the Theory of Groups, vol. 148. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4612-4176-8"},{"key":"41_CR28","unstructured":"Tonnaer, L., Perez Rey, L.A., Menkovski, V., Holenderski, M., Portegies, J.: Quantifying and learning linear symmetry-based disentanglement. In: International Conference on Machine Learning (2022)"},{"key":"41_CR29","doi-asserted-by":"crossref","unstructured":"Worrall, D.E., Garbin, S.J., Turmukhambetov, D., Brostow, G.J.: Interpretable transformations with encoder-decoder networks. In: International Conference on Computer Vision (2017)","DOI":"10.1109\/ICCV.2017.611"},{"key":"41_CR30","unstructured":"Wu, Z., et al.: 3D shapenets: a deep representation for volumetric shapes. In: IEEE Conference on Computer Vision and Pattern Recognition (2015)"}],"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\/978-3-031-43421-1_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,17]],"date-time":"2023-09-17T20:48:28Z","timestamp":1694983708000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-43421-1_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031434204","9783031434211"],"references-count":30,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-43421-1_41","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"18 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The work presented in this paper consists of a theoretical and practical analysis on learning representations that capture the information about symmetry transformations observed in data. Due to the nature of this work as fundamental research, it is challenging to determine any direct adverse ethical implications that might arise. However, we think that any possible ethical implications of these ideas would be a consequence of the possible applications to augmented reality, object recognition, or reinforcement learning among others. The datasets used in this work consist of procedurally generated images with no personal or sensitive information.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Statement"}},{"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":"Turin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Italy","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2023.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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"829","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":"196","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":"3.63","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":"4.5","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":"Applied Data Science Track: 239 submissions, 58 accepted papers; Demo Track: 31 submissions, 16 accepted papers.","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)"}}]}}