Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Sung Woo | - |
dc.contributor.author | Shu, Dong Wook | - |
dc.contributor.author | Kwon, Junseok | - |
dc.date.accessioned | 2022-05-19T10:40:37Z | - |
dc.date.available | 2022-05-19T10:40:37Z | - |
dc.date.issued | 2021-07 | - |
dc.identifier.issn | 2640-3498 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/57834 | - |
dc.description.abstract | In this paper, we present a novel generative adversarial network (GAN) that can describe Markovian temporal dynamics. To generate stochastic sequential data, we introduce a novel stochastic differential equation-based conditional generator and spatial-temporal constrained discriminator networks. To stabilize the learning dynamics of the min-max type of the GAN objective function, we propose well-posed constraint terms for both networks. We also propose a novel conditional Markov Wasserstein distance to induce a pathwise Wasserstein distance. The experimental results demonstrate that our method outperforms state-of-the-art methods using several different types of data. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | JMLR-JOURNAL MACHINE LEARNING RESEARCH | - |
dc.title | Generative Adversarial Networks for Markovian Temporal Dynamics: Stochastic Continuous Data Generation | - |
dc.type | Article | - |
dc.identifier.bibliographicCitation | INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139, v.139 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.wosid | 000768182704053 | - |
dc.citation.title | INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 139 | - |
dc.citation.volume | 139 | - |
dc.type.docType | Proceedings Paper | - |
dc.publisher.location | 미국 | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.description.journalRegisteredClass | other | - |
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