Results 271 to 280 of about 204,781 (309)

Using convolutional neural networks with late fusion to predict heart disease. [PDF]

Sci Rep
AlSekait DM, Zakariah M, Amin SU, Dubey P, Khan ZI.   +4 more
europepmc   +1 more source

Investigating performance and key factors for real-world deployment of grain image classification using convolutional neural networks. [PDF]

Sci Rep
Kumari R, Persson J, Eklöf VE, Källgren M, Sintorn IM.   +4 more
europepmc   +1 more source

Convolutional neural networks using preoperative CT to predict short-term recurrence after incisional hernia repair. [PDF]

Sci Rep
Xing X, Zhao B, Wang M, Liu Y.
europepmc   +1 more source

Quantifying sensor contribution in vibration-based structural health monitoring using explainable multichannel convolutional neural networks. [PDF]

Sci Rep
Dadoulis GI, Manolis GD.
europepmc   +1 more source

Correction for Anand et al., Convolutional neural networks outperform other presence-only species distribution modeling algorithms. [PDF]

Proc Natl Acad Sci U S A
europepmc   +1 more source

Differential convolutional neural network [PDF]

Neural Networks, 2019
Convolutional neural networks with strong representation ability of deep structures have ever increasing popularity in many research areas. The main difference of Convolutional Neural Networks with respect to existing similar artificial neural networks is the inclusion of the convolutional part.
Mehmet Sarigul, Buse Melis Özyildirim, Mutlu Avci   +2 more
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Factorized Convolutional Neural Networks

2017 IEEE International Conference on Computer Vision Workshops (ICCVW), 2017
In this paper, we propose to factorize the convolutional layer to reduce its computation. The 3D convolution operation in a convolutional layer can be considered as performing spatial convolution in each channel and linear projection across channels simultaneously.
Wang, Min, Liu, Baoyuan, Foroosh, Hassan
openaire   +3 more sources

Attentiondrop for Convolutional Neural Networks

2019 IEEE International Conference on Multimedia and Expo (ICME), 2019
Dropout has been widely used in fully connected networks but becomes less effective for convolutional neural networks (CNNs), since the spatially correlated features still allow dropped information to flow through the network. To make dropout more practical for CNNs, structured dropout methods have been recently proposed by dropping regions with fixed ...
Zhihao Ouyang, Yan Feng, Zihao He, Tianbo Hao, Tao Dai 0001, Shu-Tao Xia   +5 more
openaire   +1 more source