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Refinado por: assunto: Algorithms remover assunto: Artificial Intelligence remover assunto: Neural Networks remover Computer Science, Theory & Methods remover Computer Science remover
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1
On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures
On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures
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On the Complexity of Neural Network Classifiers: A Comparison Between Shallow and Deep Architectures

Bianchini, Monica ; Scarselli, Franco

IEEE transaction on neural networks and learning systems, 2014-08, Vol.25 (8), p.1553-1565

New York, NY: IEEE

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2
Nacc-Guard: a lightweight DNN accelerator architecture for secure deep learning
Nacc-Guard: a lightweight DNN accelerator architecture for secure deep learning
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Nacc-Guard: a lightweight DNN accelerator architecture for secure deep learning

Li, Peng ; Che, Cheng ; Hou, Rui

The Journal of supercomputing, 2024-03, Vol.80 (5), p.5815-5831 [Periódico revisado por pares]

New York: Springer US

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3
Accelerating neural network architecture search using multi-GPU high-performance computing
Accelerating neural network architecture search using multi-GPU high-performance computing
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Accelerating neural network architecture search using multi-GPU high-performance computing

Lupión, Marcos ; Cruz, N. C. ; Sanjuan, Juan F. ; Paechter, B. ; Ortigosa, Pilar M.

The Journal of supercomputing, 2023-05, Vol.79 (7), p.7609-7625 [Periódico revisado por pares]

New York: Springer US

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4
Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness
Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness
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Bidirectional Extreme Learning Machine for Regression Problem and Its Learning Effectiveness

Yang, Yimin ; Wang, Yaonan ; Yuan, Xiaofang

IEEE transaction on neural networks and learning systems, 2012-09, Vol.23 (9), p.1498-1505

New York, NY: IEEE

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5
Low-Complexity Approximate Convolutional Neural Networks
Low-Complexity Approximate Convolutional Neural Networks
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Low-Complexity Approximate Convolutional Neural Networks

Cintra, Renato J. ; Duffner, Stefan ; Garcia, Christophe ; Leite, Andre

IEEE transaction on neural networks and learning systems, 2018-12, Vol.29 (12), p.5981-5992

United States: IEEE

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6
The predicting public sentiment evolution on public emergencies under deep learning and internet of things
The predicting public sentiment evolution on public emergencies under deep learning and internet of things
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The predicting public sentiment evolution on public emergencies under deep learning and internet of things

Chen, Min ; Du, Wenhu

The Journal of supercomputing, 2023-04, Vol.79 (6), p.6452-6470 [Periódico revisado por pares]

New York: Springer US

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7
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions
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Toolflows for Mapping Convolutional Neural Networks on FPGAs: A Survey and Future Directions

Venieris, Stylianos I. ; Kouris, Alexandros ; Bouganis, Christos-Savvas

ACM computing surveys, 2019-05, Vol.51 (3), p.1-39 [Periódico revisado por pares]

Baltimore: Association for Computing Machinery

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8
Online mode development of Korean art learning in the post-epidemic era based on artificial intelligence and deep learning
Online mode development of Korean art learning in the post-epidemic era based on artificial intelligence and deep learning
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Online mode development of Korean art learning in the post-epidemic era based on artificial intelligence and deep learning

Deng, Kaiyi ; Wang, Guanen

The Journal of supercomputing, 2024-04, Vol.80 (6), p.8505-8528 [Periódico revisado por pares]

New York: Springer US

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9
A Multiobjective Sparse Feature Learning Model for Deep Neural Networks
A Multiobjective Sparse Feature Learning Model for Deep Neural Networks
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A Multiobjective Sparse Feature Learning Model for Deep Neural Networks

Gong, Maoguo ; Liu, Jia ; Li, Hao ; Cai, Qing ; Su, Linzhi

IEEE transaction on neural networks and learning systems, 2015-12, Vol.26 (12), p.3263-3277

United States: IEEE

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10
TentISSA-BPNN: a novel evaluation model for cloud service providers for petroleum enterprises
TentISSA-BPNN: a novel evaluation model for cloud service providers for petroleum enterprises
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TentISSA-BPNN: a novel evaluation model for cloud service providers for petroleum enterprises

Hou, Ke ; Sun, Jianping ; Guo, Mingcheng ; Pang, Ming ; Wang, Na

The Journal of supercomputing, 2024-05, Vol.80 (7), p.9162-9193 [Periódico revisado por pares]

New York: Springer US

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