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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Bacellar, A. T. L., Susskind, Z., Villon, L. A. Q. , Miranda, I. D. S., Araújo, L. S. de., Dutra, D. L. C....França, F. M. G. (2022). Distributive thermometer: A new unary encoding for weightless neural networks. In ESANN 2022 proceedings. (pp. 31-36). Bruges (online): ESANN.
Exportar Referência (IEEE) 
A. T. Bacellar et al.,  "Distributive thermometer: A new unary encoding for weightless neural networks", in ESANN 2022 proceedings, Bruges (online), ESANN, 2022, pp. 31-36
Exportar BibTeX 
@inproceedings{bacellar2022_1730780339778,
	author = "Bacellar, A. T. L. and Susskind, Z. and Villon, L. A. Q.  and Miranda, I. D. S. and Araújo, L. S. de. and Dutra, D. L. C. and Breternitz Jr, M. and John, L. K. and Lima, P. M. V. and França, F. M. G.",
	title = "Distributive thermometer: A new unary encoding for weightless neural networks",
	booktitle = "ESANN 2022 proceedings",
	year = "2022",
	editor = "",
	volume = "",
	number = "",
	series = "",
	doi = "10.14428/esann/2022.ES2022-94",
	pages = "31-36",
	publisher = "ESANN",
	address = "Bruges (online)",
	organization = "",
	url = "https://www.esann.org/proceedings/2022"
}
Exportar RIS 
TY  - CPAPER
TI  - Distributive thermometer: A new unary encoding for weightless neural networks
T2  - ESANN 2022 proceedings
AU  - Bacellar, A. T. L.
AU  - Susskind, Z.
AU  - Villon, L. A. Q. 
AU  - Miranda, I. D. S.
AU  - Araújo, L. S. de.
AU  - Dutra, D. L. C.
AU  - Breternitz Jr, M.
AU  - John, L. K.
AU  - Lima, P. M. V.
AU  - França, F. M. G.
PY  - 2022
SP  - 31-36
DO  - 10.14428/esann/2022.ES2022-94
CY  - Bruges (online)
UR  - https://www.esann.org/proceedings/2022
AB  - 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.
ER  -