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Refinado por: Nome da Publicação: Neurocomputing remover
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Focal and efficient IOU loss for accurate bounding box regression
Focal and efficient IOU loss for accurate bounding box regression
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Focal and efficient IOU loss for accurate bounding box regression

Zhang, Yi-Fan ; Ren, Weiqiang ; Zhang, Zhang ; Jia, Zhen ; Wang, Liang ; Tan, Tieniu

Neurocomputing (Amsterdam), 2022-09, Vol.506, p.146-157 [Periódico revisado por pares]

Elsevier B.V

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Feature selection in machine learning: A new perspective
Feature selection in machine learning: A new perspective
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Feature selection in machine learning: A new perspective

Cai, Jie ; Luo, Jiawei ; Wang, Shulin ; Yang, Sheng

Neurocomputing (Amsterdam), 2018-07, Vol.300, p.70-79 [Periódico revisado por pares]

Elsevier B.V

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3
Emotional state classification from EEG data using machine learning approach
Emotional state classification from EEG data using machine learning approach
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Emotional state classification from EEG data using machine learning approach

Wang, Xiao-Wei ; Nie, Dan ; Lu, Bao-Liang

Neurocomputing (Amsterdam), 2014-04, Vol.129, p.94-106 [Periódico revisado por pares]

Amsterdam: Elsevier B.V

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4
A comprehensive survey on support vector machine classification: Applications, challenges and trends
A comprehensive survey on support vector machine classification: Applications, challenges and trends
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A comprehensive survey on support vector machine classification: Applications, challenges and trends

Cervantes, Jair ; Garcia-Lamont, Farid ; Rodríguez-Mazahua, Lisbeth ; Lopez, Asdrubal

Neurocomputing (Amsterdam), 2020-09, Vol.408, p.189-215 [Periódico revisado por pares]

Elsevier B.V

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5
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
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Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)

Christ, Maximilian ; Braun, Nils ; Neuffer, Julius ; Kempa-Liehr, Andreas W.

Neurocomputing (Amsterdam), 2018-09, Vol.307, p.72-77 [Periódico revisado por pares]

Elsevier B.V

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6
On hyperparameter optimization of machine learning algorithms: Theory and practice
On hyperparameter optimization of machine learning algorithms: Theory and practice
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On hyperparameter optimization of machine learning algorithms: Theory and practice

Yang, Li ; Shami, Abdallah

Neurocomputing (Amsterdam), 2020-11, Vol.415, p.295-316 [Periódico revisado por pares]

Elsevier B.V

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7
Pruning and quantization for deep neural network acceleration: A survey
Pruning and quantization for deep neural network acceleration: A survey
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Pruning and quantization for deep neural network acceleration: A survey

Liang, Tailin ; Glossner, John ; Wang, Lei ; Shi, Shaobo ; Zhang, Xiaotong

Neurocomputing (Amsterdam), 2021-10, Vol.461, p.370-403 [Periódico revisado por pares]

Elsevier B.V

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8
GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification
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GAN-based synthetic medical image augmentation for increased CNN performance in liver lesion classification

Frid-Adar, Maayan ; Diamant, Idit ; Klang, Eyal ; Amitai, Michal ; Goldberger, Jacob ; Greenspan, Hayit

Neurocomputing (Amsterdam), 2018-12, Vol.321, p.321-331 [Periódico revisado por pares]

Elsevier B.V

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9
A review on the attention mechanism of deep learning
A review on the attention mechanism of deep learning
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A review on the attention mechanism of deep learning

Niu, Zhaoyang ; Zhong, Guoqiang ; Yu, Hui

Neurocomputing (Amsterdam), 2021-09, Vol.452, p.48-62 [Periódico revisado por pares]

Elsevier B.V

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10
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
Understanding adversarial training: Increasing local stability of supervised models through robust optimization
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Understanding adversarial training: Increasing local stability of supervised models through robust optimization

Shaham, Uri ; Yamada, Yutaro ; Negahban, Sahand

Neurocomputing (Amsterdam), 2018-09, Vol.307, p.195-204 [Periódico revisado por pares]

Elsevier B.V

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