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dc.contributor.authorAdali, Tülay
dc.contributor.authorGuido, Rodrigo Capobianco
dc.contributor.authorHo, Tin Kam
dc.contributor.authorMüller, Klaus-Robert
dc.contributor.authorStrother, Stephen
dcterms.creatorhttps://orcid.org/0000-0003-0594-2796en_US
dc.date.accessioned2022-08-05T20:49:43Z
dc.date.available2022-08-05T20:49:43Z
dc.date.issued2022-06-28
dc.description.abstractMost of the work we do in signal processing these days is data driven. The shift from the more traditional and model-driven approaches to those that are data driven has also underlined the importance of explainability of our solutions. Because most traditional signal processing approaches start with a number of modeling assumptions, they are comprehensible by the very nature of their construction. However, this is not necessarily the case when we choose to rely more heavily on the data and minimize modeling assumptions.en_US
dc.description.urihttps://ieeexplore.ieee.org/document/9810056en_US
dc.format.extent3 pagesen_US
dc.genrejournal articlesen_US
dc.genrepostprintsen_US
dc.identifierdoi:10.13016/m2ebbk-fvdp
dc.identifier.citationT. Adali, R. C. Guido, T. K. Ho, K. -R. Müller and S. Strother, "Interpretability, Reproducibility, and Replicability [From the Guest Editors]," in IEEE Signal Processing Magazine, vol. 39, no. 4, pp. 5-7, July 2022, doi: 10.1109/MSP.2022.3170665.en_US
dc.identifier.urihttps://doi.org/10.1109/MSP.2022.3170665
dc.identifier.urihttp://hdl.handle.net/11603/25289
dc.language.isoen_USen_US
dc.publisherIEEEen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rights© 2022 IEEE.  Personal use of this material is permitted.  Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.titleInterpretability, Reproducibility, and Replicabilityen_US
dc.title.alternativeIntroduction for the Special Issue on Explainability in Data Science: Interpretability, Reproducibility, and Replicabilityen_US
dc.typeTexten_US


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