{"arxiv":1,"conference":{"name":"NeurIPS: Neural Information Processing Systems","start_date":"2024-12-09","end_date":"2024-12-15","location":"Vancouver, Canada"},"_id":"19515","day":"20","publisher":"Neural Information Processing Systems Foundation","quality_controlled":"1","publication_status":"published","month":"12","language":[{"iso":"eng"}],"date_published":"2024-12-20T00:00:00Z","volume":37,"project":[{"grant_number":"101034413","name":"IST-BRIDGE: International postdoctoral program","call_identifier":"H2020","_id":"fc2ed2f7-9c52-11eb-aca3-c01059dda49c"}],"acknowledgement":"MF is supported by the MSCA IST-Bridge fellowship which has received funding from the European Union’s Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No 101034413. ER and VM are supported by the PNRR MUR project PE0000013-FAIR. MP is supported by the Sapienza grant \"Predicting and Explaining Clinical Trial Outcomes\", prot. RG12218166FA3F13.","intvolume":"        37","abstract":[{"text":"Neural models learn data representations that lie on low-dimensional manifolds,\r\nyet modeling the relation between these representational spaces is an ongoing challenge. By integrating spectral geometry principles into neural modeling, we show\r\nthat this problem can be better addressed in the functional domain, mitigating complexity, while enhancing interpretability and performances on downstream tasks.\r\nTo this end, we introduce a multi-purpose framework to the representation learning\r\ncommunity, which allows to: (i) compare different spaces in an interpretable way\r\nand measure their intrinsic similarity; (ii) find correspondences between them, both\r\nin unsupervised and weakly supervised settings, and (iii) to effectively transfer\r\nrepresentations between distinct spaces. We validate our framework on various\r\napplications, ranging from stitching to retrieval tasks, and on multiple modalities,\r\ndemonstrating that Latent Functional Maps can serve as a swiss-army knife for\r\nrepresentation alignment","lang":"eng"}],"citation":{"short":"M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, E. Rodolà, in:, 38th Conference on Neural Information Processing Systems, Neural Information Processing Systems Foundation, 2024.","apa":"Fumero, M., Pegoraro, M., Maiorca, V., Locatello, F., &#38; Rodolà, E. (2024). Latent functional maps: A spectral framework for representation alignment. In <i>38th Conference on Neural Information Processing Systems</i> (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.","mla":"Fumero, Marco, et al. “Latent Functional Maps: A Spectral Framework for Representation Alignment.” <i>38th Conference on Neural Information Processing Systems</i>, vol. 37, Neural Information Processing Systems Foundation, 2024.","chicago":"Fumero, Marco, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, and Emanuele Rodolà. “Latent Functional Maps: A Spectral Framework for Representation Alignment.” In <i>38th Conference on Neural Information Processing Systems</i>, Vol. 37. Neural Information Processing Systems Foundation, 2024.","ama":"Fumero M, Pegoraro M, Maiorca V, Locatello F, Rodolà E. Latent functional maps: A spectral framework for representation alignment. In: <i>38th Conference on Neural Information Processing Systems</i>. Vol 37. Neural Information Processing Systems Foundation; 2024.","ista":"Fumero M, Pegoraro M, Maiorca V, Locatello F, Rodolà E. 2024. Latent functional maps: A spectral framework for representation alignment. 38th Conference on Neural Information Processing Systems. NeurIPS: Neural Information Processing Systems, Advances in Neural Information Processing Systems, vol. 37.","ieee":"M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, and E. Rodolà, “Latent functional maps: A spectral framework for representation alignment,” in <i>38th Conference on Neural Information Processing Systems</i>, Vancouver, Canada, 2024, vol. 37."},"oa":1,"scopus_import":"1","OA_type":"green","publication_identifier":{"issn":["1049-5258"]},"publication":"38th Conference on Neural Information Processing Systems","status":"public","year":"2024","main_file_link":[{"url":"https://doi.org/10.48550/arXiv.2406.14183","open_access":"1"}],"ec_funded":1,"alternative_title":["Advances in Neural Information Processing Systems"],"OA_place":"repository","article_processing_charge":"No","author":[{"full_name":"Fumero, Marco","last_name":"Fumero","first_name":"Marco","id":"1c1593eb-393f-11ef-bb8e-ab4f1e979650"},{"first_name":"Marco","last_name":"Pegoraro","full_name":"Pegoraro, Marco"},{"first_name":"Valentino","last_name":"Maiorca","full_name":"Maiorca, Valentino"},{"full_name":"Locatello, Francesco","id":"26cfd52f-2483-11ee-8040-88983bcc06d4","last_name":"Locatello","first_name":"Francesco","orcid":"0000-0002-4850-0683"},{"first_name":"Emanuele","last_name":"Rodolà","full_name":"Rodolà, Emanuele"}],"department":[{"_id":"FrLo"}],"date_updated":"2025-05-14T11:36:51Z","external_id":{"arxiv":["2406.14183"]},"type":"conference","oa_version":"Preprint","date_created":"2025-04-06T22:01:32Z","corr_author":"1","title":"Latent functional maps: A spectral framework for representation alignment","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"}