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Prospects Overview of the Superconducting Neural Networks

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dc.contributor.author LUPU, Maria
dc.date.accessioned 2022-12-27T14:26:08Z
dc.date.available 2022-12-27T14:26:08Z
dc.date.issued 2022
dc.identifier.citation LUPU, Maria. Prospects Overview of the Superconducting Neural Networks. In: Electronics, Communications and Computing (IC ECCO-2022): 12th intern. conf., 20-21 Oct. 2022, Chişinău, Republica Moldova: conf. proc., Chişinău, 2022, pp. 98-101. en_US
dc.identifier.uri https://doi.org/10.52326/ic-ecco.2022/EL.07
dc.identifier.uri http://repository.utm.md/handle/5014/21836
dc.description.abstract The long-term efforts of many research groups have led to the fact that by now a large number of different "learning rules" and architectures of neural networks, their hardware implementations and methods of using neural networks to solve applied problems have been accumulated. These intellectual inventions exist in the form of a "technopark" of neural networks. Each network from the technopark has its own architecture, training rules and solves a certain set of tasks. Moreover, specialized high-speed devices can be created on its basis. There are several levels of alienation of a neural network from a universal computer: from network learning on a universal device and the use of rich possibilities in manipulating a task book, learning algorithms and modifying architecture, to complete alienation without learning and modification capabilities, only the functioning of a trained network. en_US
dc.language.iso en en_US
dc.publisher Technical University of Moldova en_US
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ *
dc.subject superconducting neural networks en_US
dc.subject dynamic processes en_US
dc.subject physics-based models en_US
dc.subject deep neural networks en_US
dc.title Prospects Overview of the Superconducting Neural Networks en_US
dc.type Article en_US


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  • 2022
    Proceedings of the 12th IC|ECCO; October 20-21, 2022

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Attribution-NonCommercial-NoDerivs 3.0 United States Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 United States

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