Interpretability, Reproducibility, and Replicability
| dc.contributor.author | Adali, Tülay | |
| dc.contributor.author | Guido, Rodrigo Capobianco | |
| dc.contributor.author | Ho, Tin Kam | |
| dc.contributor.author | Müller, Klaus-Robert | |
| dc.contributor.author | Strother, Stephen | |
| dc.date.accessioned | 2022-08-05T20:49:43Z | |
| dc.date.available | 2022-08-05T20:49:43Z | |
| dc.date.issued | 2022-06-28 | |
| dc.description.abstract | Most 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.uri | https://ieeexplore.ieee.org/document/9810056 | en_US |
| dc.format.extent | 3 pages | en_US |
| dc.genre | journal articles | en_US |
| dc.genre | postprints | en_US |
| dc.identifier | doi:10.13016/m2ebbk-fvdp | |
| dc.identifier.citation | T. 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.uri | https://doi.org/10.1109/MSP.2022.3170665 | |
| dc.identifier.uri | http://hdl.handle.net/11603/25289 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | IEEE | en_US |
| dc.relation.isAvailableAt | The University of Maryland, Baltimore County (UMBC) | |
| dc.relation.ispartof | UMBC Computer Science and Electrical Engineering Department Collection | |
| dc.relation.ispartof | UMBC 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.title | Interpretability, Reproducibility, and Replicability | en_US |
| dc.title.alternative | Introduction for the Special Issue on Explainability in Data Science: Interpretability, Reproducibility, and Replicability | en_US |
| dc.type | Text | en_US |
| dcterms.creator | https://orcid.org/0000-0003-0594-2796 | en_US |