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<titleInfo><title>Latent functional maps: A spectral framework for representation alignment</title></titleInfo>

  
  
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  <title>Advances in Neural Information Processing Systems</title>
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<note type="publicationStatus">published</note>


<note type="qualityControlled">yes</note>

<name type="personal">
  <namePart type="given">Marco</namePart>
  <namePart type="family">Fumero</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">1c1593eb-393f-11ef-bb8e-ab4f1e979650</identifier></name>
<name type="personal">
  <namePart type="given">Marco</namePart>
  <namePart type="family">Pegoraro</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Valentino</namePart>
  <namePart type="family">Maiorca</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>
<name type="personal">
  <namePart type="given">Francesco</namePart>
  <namePart type="family">Locatello</namePart>
  <role><roleTerm type="text">author</roleTerm> </role><identifier type="local">26cfd52f-2483-11ee-8040-88983bcc06d4</identifier><description xsi:type="identifierDefinition" type="orcid">0000-0002-4850-0683</description></name>
<name type="personal">
  <namePart type="given">Emanuele</namePart>
  <namePart type="family">Rodolà</namePart>
  <role><roleTerm type="text">author</roleTerm> </role></name>







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<name type="conference">
  <namePart>NeurIPS: Neural Information Processing Systems</namePart>
</name>



<name type="corporate">
  <namePart>IST-BRIDGE: International postdoctoral program</namePart>
  <role><roleTerm type="text">project</roleTerm></role>
</name>



<abstract lang="eng">Neural models learn data representations that lie on low-dimensional manifolds,
yet modeling the relation between these representational spaces is an ongoing challenge. By integrating spectral geometry principles into neural modeling, we show
that this problem can be better addressed in the functional domain, mitigating complexity, while enhancing interpretability and performances on downstream tasks.
To this end, we introduce a multi-purpose framework to the representation learning
community, which allows to: (i) compare different spaces in an interpretable way
and measure their intrinsic similarity; (ii) find correspondences between them, both
in unsupervised and weakly supervised settings, and (iii) to effectively transfer
representations between distinct spaces. We validate our framework on various
applications, ranging from stitching to retrieval tasks, and on multiple modalities,
demonstrating that Latent Functional Maps can serve as a swiss-army knife for
representation alignment</abstract>

<originInfo><publisher>Neural Information Processing Systems Foundation</publisher><dateIssued encoding="w3cdtf">2024</dateIssued><place><placeTerm type="text">Vancouver, Canada</placeTerm></place>
</originInfo>
<language><languageTerm authority="iso639-2b" type="code">eng</languageTerm>
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<relatedItem type="host"><titleInfo><title>38th Conference on Neural Information Processing Systems</title></titleInfo>
  <identifier type="issn">1049-5258</identifier>
  <identifier type="arXiv">2406.14183</identifier>
<part><detail type="volume"><number>37</number></detail>
</part>
</relatedItem>


<extension>
<bibliographicCitation>
<chicago>Fumero, Marco, Marco Pegoraro, Valentino Maiorca, Francesco Locatello, and Emanuele Rodolà. “Latent Functional Maps: A Spectral Framework for Representation Alignment.” In &lt;i&gt;38th Conference on Neural Information Processing Systems&lt;/i&gt;, Vol. 37. Neural Information Processing Systems Foundation, 2024.</chicago>
<ama>Fumero M, Pegoraro M, Maiorca V, Locatello F, Rodolà E. Latent functional maps: A spectral framework for representation alignment. In: &lt;i&gt;38th Conference on Neural Information Processing Systems&lt;/i&gt;. Vol 37. Neural Information Processing Systems Foundation; 2024.</ama>
<apa>Fumero, M., Pegoraro, M., Maiorca, V., Locatello, F., &amp;#38; Rodolà, E. (2024). Latent functional maps: A spectral framework for representation alignment. In &lt;i&gt;38th Conference on Neural Information Processing Systems&lt;/i&gt; (Vol. 37). Vancouver, Canada: Neural Information Processing Systems Foundation.</apa>
<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.</ista>
<mla>Fumero, Marco, et al. “Latent Functional Maps: A Spectral Framework for Representation Alignment.” &lt;i&gt;38th Conference on Neural Information Processing Systems&lt;/i&gt;, vol. 37, Neural Information Processing Systems Foundation, 2024.</mla>
<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.</short>
<ieee>M. Fumero, M. Pegoraro, V. Maiorca, F. Locatello, and E. Rodolà, “Latent functional maps: A spectral framework for representation alignment,” in &lt;i&gt;38th Conference on Neural Information Processing Systems&lt;/i&gt;, Vancouver, Canada, 2024, vol. 37.</ieee>
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