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有關 Capabilities of a structured neural network. Learning and comparison with classical techniques. 的學術文章 | |
Neural networks and deep learning - Aggarwal - 3871 個引述 Deep neural networks for structured data - Bianchini - 25 個引述 A survey of deep neural network architectures and their … - Liu - 3821 個引述 | |
Capabilities of a structured neural network. Learning and ...
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PDF | On Jan 1, 1994, Joan Codina and others published Capabilities of a structured neural network. Learning and comparison with classical techniques.
(PDF) Capabilities of a structured neural network. Learning and ...
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Capabilities of a structured neural network. Learning and comparison with classical techniques. ... Learning and comparison with classical techniques.
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Classical-to-quantum convolutional neural network transfer ...
ScienceDirect.com
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由 J Kim 著作2023被引用 29 次 — We propose transfer learning as an effective strategy for utilizing small QCNNs in the noisy intermediate-scale quantum era to the full extent.
A Comparative Analysis of Hybrid-Quantum Classical ...
arXiv
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2024年2月16日 — This paper performs an extensive comparative analysis between different hybrid quantum-classical machine learning algorithms.
Comparison of different input modalities and network ...
Nature
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由 KO Cho 著作2020被引用 91 次 — In the present study, we systematically compared the performance of different combinations of input modalities and network structures on a fixed window size ...
Structurally Flexible Neural Networks: Evolving the ...
arXiv
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2024年5月17日 — In this paper, we present Structurally Flexible Neural Networks (SFNNs), which consist of connected gated recurrent units (GRUs) as synaptic plasticity rules ...
Deep Learning: A Comprehensive Overview on ...
Springer
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由 IH Sarker 著作2021被引用 2080 次 — This article presents a structured and comprehensive view on DL techniques including a taxonomy considering various types of real-world tasks like supervised ...
A Comparison of Classical Identification and Learning ...
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PDF | This paper proposes a comparative study on the predictive accuracy of some classical system identification techniques with respect to recent.
Scalars are universal: Equivariant machine learning, ...
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由 S Villar 著作被引用 135 次 — Neural networks can be designed to parameterize classes of functions satisfying different forms of symmetries, from the classical (ap- proximately) translation ...
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Graph neural networks: A review of methods and applications
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由 J Zhou 著作2020被引用 6820 次 — Graph neural networks (GNNs) are neural models that capture the dependence of graphs via message passing between the nodes of graphs.
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