Zobrazeno 1 - 10
of 4 196
pro vyhledávání: '"MNIST database"'
Publikováno v:
Adaptivni Sistemi Avtomatičnogo Upravlinnâ, Vol 2, Iss 41 (2022)
It is known that the use of a multilayer perceptron with a traditional structure in solving real problems of image recognition and classification causes certain difficulties, in particular, associated with a large image dimension (this significantly
Externí odkaz:
https://doaj.org/article/1eb3301452bc4f1f915350d001486132
Akademický článek
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Akademický článek
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Publikováno v:
Frontiers in Artificial Intelligence, Vol 5 (2022)
A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification
Externí odkaz:
https://doaj.org/article/54e777148e4140fba95880fb9be1a529
Publikováno v:
IEEE Access, Vol 7, Pp 68316-68330 (2019)
The modeling and optimization method (MAOM) proposed in this study finds the best combination of parameters for a multi-layer convolutional neural network (MCNN). This study emphasizes that in addition to the importance of the MCNN structure, the par
Externí odkaz:
https://doaj.org/article/c87dd83dcd164ee092e1909eda7fd158
Autor:
Mingtao Zhan, Yongpan Liu, Xiyuan Tang, David Z. Pan, Keren Zhu, Jaydeep P. Kulkarni, Nan Sun, Meizhi Wang, Xiangxing Yang, Nanshu Lu
Publikováno v:
CICC
AI edge devices require local intelligence for the concerns of latency and privacy. Given the accuracy and energy constraints, low-power convolutional neural networks (CNNs) are gaining popularity. To alleviate the high memory access energy and compu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::787c64a648b8098237df501f9c1ef935
https://doi.org/10.1109/jssc.2023.3238725
https://doi.org/10.1109/jssc.2023.3238725
Publikováno v:
IEEE Internet of Things Journal. 10:2141-2151
Artificial Intelligence of Things (AIoT), as a fusion of AI and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2b739a7d832af9f41094a6e4442b4a0
https://doi.org/10.1109/jiot.2021.3081606
https://doi.org/10.1109/jiot.2021.3081606
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 34:394-408
Spiking neural networks (SNNs) are brain-inspired mathematical models with the ability to process information in the form of spikes. SNNs are expected to provide not only new machine-learning algorithms but also energy-efficient computational models
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::de0977b33a691c47088b2c1b283e309d
https://doi.org/10.1109/tnnls.2021.3095068
https://doi.org/10.1109/tnnls.2021.3095068
Publikováno v:
IEEE Transactions on Dependable and Secure Computing. 20:147-160
Federated learning allows a large number of resource-constrained clients to train a globally-shared model together without sharing local data. These clients usually have only a few classes (categories) of data for training, where the data distributio
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0fc51bc4c4da11750c56b1cb501a991b
https://doi.org/10.1109/tdsc.2021.3128679
https://doi.org/10.1109/tdsc.2021.3128679
Publikováno v:
IEEE Internet of Things Journal. 9:22147-22157
Federated Learning (FL) is an emerging paradigm through which decentralized devices can collaboratively train a common model. However, a serious concern is the leakage of privacy from exchanged gradient information between clients and the parameter s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5a761f83d980208850cc7a26a0fee846
https://doi.org/10.1109/jiot.2021.3102030
https://doi.org/10.1109/jiot.2021.3102030