Utilize este identificador para referenciar este registo:
http://hdl.handle.net/10071/26524
Registo completo
Campo DC | Valor | Idioma |
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dc.contributor.author | Bacellar, A. T. L. | - |
dc.contributor.author | Susskind, Z. | - |
dc.contributor.author | Villon, L. A. Q. | - |
dc.contributor.author | Miranda, I. D. S. | - |
dc.contributor.author | Araújo, L. S. de. | - |
dc.contributor.author | Dutra, D. L. C. | - |
dc.contributor.author | Breternitz Jr, M. | - |
dc.contributor.author | John, L. K. | - |
dc.contributor.author | Lima, P. M. V. | - |
dc.contributor.author | França, F. M. G. | - |
dc.date.accessioned | 2022-12-05T12:46:30Z | - |
dc.date.available | 2022-12-05T12:46:30Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Bacellar, A. T. L., Susskind, Z., Villon, L. A. Q., Miranda, I. D. S., Araújo, L. S. de., Dutra, D. L. C., Breternitz Jr., M., John, L. K., Lima, P. M. V., & França, F. M. G. (2022). Distributive thermometer: A new unary encoding for weightless neural networks. In ESANN 2022 proceedings (pp. 31-36). https://doi.org/10.14428/esann/2022.ES2022-94 | - |
dc.identifier.isbn | 978287587 084-1 | - |
dc.identifier.uri | http://hdl.handle.net/10071/26524 | - |
dc.description.abstract | The binary encoding of real valued inputs is a crucial part of Weightless Neural Networks. The Linear Thermometer and its variations are the most prominent methods to determine binary encoding for input data but, as they make assumptions about the input distribution, the resulting encoding is sub-optimal and possibly wasteful when the assumption is incorrect. We propose a new thermometer approach that doesn’t require such assumptions. Our results show that it achieves similar or better accuracy when compared to a thermometer that correctly assumes the distribution, and accuracy gains up to 26.3% when other thermometer representations assume an unsound distribution. | eng |
dc.language.iso | eng | - |
dc.publisher | ESANN | - |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04466%2F2020/PT | - |
dc.relation | POCI-01-0247-FEDER-045912 | - |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDP%2F04466%2F2020/PT | - |
dc.relation.ispartof | ESANN 2022 proceedings | - |
dc.rights | openAccess | - |
dc.title | Distributive thermometer: A new unary encoding for weightless neural networks | eng |
dc.type | conferenceObject | - |
dc.event.title | 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning | - |
dc.event.type | Conferência | pt |
dc.event.location | Bruges (online) | eng |
dc.event.date | 2022 | - |
dc.pagination | 31 - 36 | - |
dc.peerreviewed | yes | - |
dc.date.updated | 2022-12-05T12:44:29Z | - |
dc.description.version | info:eu-repo/semantics/publishedVersion | - |
dc.identifier.doi | 10.14428/esann/2022.ES2022-94 | - |
dc.subject.fos | Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação | por |
iscte.identifier.ciencia | https://ciencia.iscte-iul.pt/id/ci-pub-91314 | - |
Aparece nas coleções: | ISTAR-CRI - Comunicações a conferências internacionais |
Ficheiros deste registo:
Ficheiro | Tamanho | Formato | |
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conferenceobject_91314.pdf | 1,54 MB | Adobe PDF | Ver/Abrir |
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