Riemannian Neural SDE: Learning Stochastic Representations on Manifolds
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sung Woo | - |
dc.contributor.author | Kim, Hyomin | - |
dc.contributor.author | Lee, Kyungjae | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.accessioned | 2023-08-30T02:52:40Z | - |
dc.date.available | 2023-08-30T02:52:40Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 1049-5258 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/67436 | - |
dc.description.abstract | In recent years, the neural stochastic differential equation (NSDE) has gained attention for modeling stochastic representations with great success in various types of applications. However, it typically loses expressivity when the data representation is manifold-valued. To address this issue, we suggest a principled method for expressing the stochastic representation with the Riemannian neural SDE (RNSDE), which extends the conventional Euclidean NSDE. Empirical results for various tasks demonstrate that the proposed method significantly outperforms baseline methods. © 2022 Neural information processing systems foundation. All rights reserved. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Neural information processing systems foundation | - |
dc.title | Riemannian Neural SDE: Learning Stochastic Representations on Manifolds | - |
dc.type | Article | - |
dc.identifier.bibliographicCitation | Advances in Neural Information Processing Systems, v.35 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85163160022 | - |
dc.citation.title | Advances in Neural Information Processing Systems | - |
dc.citation.volume | 35 | - |
dc.type.docType | Conference paper | - |
dc.publisher.location | 미국 | - |
dc.description.journalRegisteredClass | scopus | - |
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