Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/26524
Author(s): | 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. |
Date: | 2022 |
Title: | Distributive thermometer: A new unary encoding for weightless neural networks |
Book title/volume: | ESANN 2022 proceedings |
Pages: | 31 - 36 |
Event title: | 30th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning |
Reference: | 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 |
ISBN: | 978287587 084-1 |
DOI (Digital Object Identifier): | 10.14428/esann/2022.ES2022-94 |
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. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Size | Format | |
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conferenceobject_91314.pdf | 1,54 MB | Adobe PDF | View/Open |
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