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A Mutually Supervised Graph Attention Network for Few-Shot Segmentation: The Perspective of Fully Utilizing Limited Samples
A Mutually Supervised Graph Attention Network for Few-Shot Segmentation: The Perspective of Fully Utilizing Limited Samples
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A Mutually Supervised Graph Attention Network for Few-Shot Segmentation: The Perspective of Fully Utilizing Limited Samples

Gao, Honghao ; Xiao, Junsheng ; Yin, Yuyu ; Liu, Tong ; Shi, Jiangang

IEEE transaction on neural networks and learning systems, 2024-04, Vol.35 (4), p.1-13

United States: IEEE

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12
Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering
Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering
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Beyond Bilinear: Generalized Multimodal Factorized High-Order Pooling for Visual Question Answering

Yu, Zhou ; Yu, Jun ; Xiang, Chenchao ; Fan, Jianping ; Tao, Dacheng

IEEE transaction on neural networks and learning systems, 2018-12, Vol.29 (12), p.5947-5959

United States: IEEE

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13
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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14
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning
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Adaptive Fuzzy Neural Network Control for a Constrained Robot Using Impedance Learning

He, Wei ; Dong, Yiting

IEEE transaction on neural networks and learning systems, 2018-04, Vol.29 (4), p.1174-1186

United States: IEEE

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15
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review
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Deep Learning for LiDAR Point Clouds in Autonomous Driving: A Review

Li, Ying ; Ma, Lingfei ; Zhong, Zilong ; Liu, Fei ; Chapman, Michael A. ; Cao, Dongpu ; Li, Jonathan

IEEE transaction on neural networks and learning systems, 2021-08, Vol.32 (8), p.3412-3432

IEEE

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16
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data
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Cost-Sensitive Learning of Deep Feature Representations From Imbalanced Data

Khan, Salman H. ; Hayat, Munawar ; Bennamoun, Mohammed ; Sohel, Ferdous A. ; Togneri, Roberto

IEEE transaction on neural networks and learning systems, 2018-08, Vol.29 (8), p.3573-3587

United States: IEEE

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17
Extreme Learning Machine for Multilayer Perceptron
Extreme Learning Machine for Multilayer Perceptron
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Extreme Learning Machine for Multilayer Perceptron

Tang, Jiexiong ; Deng, Chenwei ; Huang, Guang-Bin

IEEE transaction on neural networks and learning systems, 2016-04, Vol.27 (4), p.809-821

United States: IEEE

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18
Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser
Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser
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Regularizing Hyperspectral and Multispectral Image Fusion by CNN Denoiser

Dian, Renwei ; Li, Shutao ; Kang, Xudong

IEEE transaction on neural networks and learning systems, 2021-03, Vol.32 (3), p.1124-1135

United States: IEEE

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19
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
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Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics

Chong Zhang ; Pin Lim ; Qin, A. K. ; Kay Chen Tan

IEEE transaction on neural networks and learning systems, 2017-10, Vol.28 (10), p.2306-2318

United States: IEEE

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20
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution
Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution
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Learning a Low Tensor-Train Rank Representation for Hyperspectral Image Super-Resolution

Dian, Renwei ; Li, Shutao ; Fang, Leyuan

IEEE transaction on neural networks and learning systems, 2019-09, Vol.30 (9), p.2672-2683

United States: IEEE

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