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pro vyhledávání: '"Bedionita, Soro"'
We propose a neural network weight encoding method for network property prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network parameters. Our approach is capable of encoding neural networks in a model zo
Externí odkaz:
http://arxiv.org/abs/2305.16625
Autor:
Bedionita Soro, Chaewoo Lee
Publikováno v:
IEEE Access, Vol 7, Pp 104892-104899 (2019)
The performance of localization methods based on the receiver signal strength (RSS) is significantly affected by the signal strength indicator's (RSSI) instability. To date, there is no adequate approach which significantly reduces the impact of such
Externí odkaz:
https://doaj.org/article/70a2274f34394fdb918021b65235efc0
We propose an approach to neural network weight encoding for generalization performance prediction that utilizes set-to-set and set-to-vector functions to efficiently encode neural network parameters. Our approach is capable of encoding neural networ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::77f72fc310d53e060231aa9ac865c0b0
http://arxiv.org/abs/2305.16625
http://arxiv.org/abs/2305.16625
Autor:
Bedionita Soro, Chaewoo Lee
Publikováno v:
Sensors, Vol 19, Iss 8, p 1790 (2019)
The performance of an Artificial Neural Network (ANN)-based algorithm is subject to the way the feature data is extracted. This is a common issue when applying the ANN to indoor fingerprinting-based localization where the signal is unstable. To date,
Externí odkaz:
https://doaj.org/article/87890e3d30e4464ea51f9fa8eb8331a6
Autor:
Chaewoo Lee, Bedionita Soro
Publikováno v:
TENCON
An indoor fingerprinting localization algorithm based on a single Artificial Neural Network (ANN) model may be subject to the Received Signal Strength Indicator (RSSI) fluctuation than multiple neural networks based fingerprinting algorithm. To date,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::191934bd1765c3fb41a9a63df48a949e
https://doi.org/10.1109/tencon.2018.8650230
https://doi.org/10.1109/tencon.2018.8650230