The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
A durabilidade descreve a capacidade de um dispositivo funcionar adequadamente em condições imperfeitas. Recentemente propusemos uma nova estrutura de rede neural chamada "Rede Neural Acessível" (AfNN), na qual neurônios acessíveis da camada oculta são considerados os elementos responsáveis pela propriedade de robustez observada na função cerebral humana. Enquanto anteriormente mostramos que os AfNNs ainda podem generalizar e aprender, aqui mostramos que essas redes são robustas contra danos que ocorrem após o término do processo de aprendizagem. Os resultados apoiam a visão de que os AfNNs incorporam a importante característica da durabilidade. Em nossa contribuição, investigamos a durabilidade do AfNN quando alguns neurônios da camada oculta são danificados após o processo de aprendizagem.
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Yoko UWATE, Yoshifumi NISHIO, Ruedi STOOP, "Durability of Affordable Neural Networks against Damaging Neurons" in IEICE TRANSACTIONS on Fundamentals,
vol. E92-A, no. 2, pp. 585-593, February 2009, doi: 10.1587/transfun.E92.A.585.
Abstract: Durability describes the ability of a device to operate properly in imperfect conditions. We have recently proposed a novel neural network structure called an "Affordable Neural Network" (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. Whereas earlier we have shown that AfNNs can still generalize and learn, here we show that these networks are robust against damages occurring after the learning process has terminated. The results support the view that AfNNs embody the important feature of durability. In our contribution, we investigate the durability of the AfNN when some of the neurons in the hidden layer are damaged after the learning process.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.E92.A.585/_p
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@ARTICLE{e92-a_2_585,
author={Yoko UWATE, Yoshifumi NISHIO, Ruedi STOOP, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Durability of Affordable Neural Networks against Damaging Neurons},
year={2009},
volume={E92-A},
number={2},
pages={585-593},
abstract={Durability describes the ability of a device to operate properly in imperfect conditions. We have recently proposed a novel neural network structure called an "Affordable Neural Network" (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. Whereas earlier we have shown that AfNNs can still generalize and learn, here we show that these networks are robust against damages occurring after the learning process has terminated. The results support the view that AfNNs embody the important feature of durability. In our contribution, we investigate the durability of the AfNN when some of the neurons in the hidden layer are damaged after the learning process.},
keywords={},
doi={10.1587/transfun.E92.A.585},
ISSN={1745-1337},
month={February},}
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TY - JOUR
TI - Durability of Affordable Neural Networks against Damaging Neurons
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 585
EP - 593
AU - Yoko UWATE
AU - Yoshifumi NISHIO
AU - Ruedi STOOP
PY - 2009
DO - 10.1587/transfun.E92.A.585
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E92-A
IS - 2
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - February 2009
AB - Durability describes the ability of a device to operate properly in imperfect conditions. We have recently proposed a novel neural network structure called an "Affordable Neural Network" (AfNN), in which affordable neurons of the hidden layer are considered as the elements responsible for the robustness property as is observed in human brain function. Whereas earlier we have shown that AfNNs can still generalize and learn, here we show that these networks are robust against damages occurring after the learning process has terminated. The results support the view that AfNNs embody the important feature of durability. In our contribution, we investigate the durability of the AfNN when some of the neurons in the hidden layer are damaged after the learning process.
ER -