Recent advances in convolutional neural networks
Jiuxiang Gu,Zhenhua Wang,Jason Kuen,Lianyang Ma,Amir Shahroudy,Bing Shuai,Ting Liu,Xingxing Wang,Gang Wang,Jianfei Cai,Tsuhan Chen +10 more
TLDR
A broad survey of the recent advances in convolutional neural networks can be found in this article, where the authors discuss the improvements of CNN on different aspects, namely, layer design, activation function, loss function, regularization, optimization and fast computation.About:
This article is published in Pattern Recognition.The article was published on 2018-05-01 and is currently open access. It has received 3125 citations till now. The article focuses on the topics: Deep learning & Convolutional neural network.read more
Citations
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Towards multi-label classification: Next step of machine learning for microbiome research.
TL;DR: In this paper, the authors summarize the typical ML approaches of single-label classification for microbiome research, and demonstrate their limitations in multi-label disease detection using a real dataset, including a series of promising strategies and key technical issues for applying multilabel classification in microbiome-based studies.
Journal ArticleDOI
Integrating Models and Fusing Data in a Deep Ensemble Learning Method for Predicting Epidemic Diseases Outbreak
TL;DR: In this paper, the authors proposed a generic data-driven method that can predict daily COVID-19 positive cases and therefore help stakeholders to make and review their epidemic response plans.
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DeepPN: a deep parallel neural network based on convolutional neural network and graph convolutional network for predicting RNA-protein binding sites
TL;DR: DeepPN as discussed by the authors is a deep parallel neural network that is constructed with a convolutional neural network (CNN) and graph convolutionsal network (GCN) for detecting RNA-binding proteins (RBPs) binding sites.
Journal ArticleDOI
Stacking Ensemble Learning-Based Convolutional Gated Recurrent Neural Network for Diabetes Miletus
G. Geetha,K. Mohana Prasad +1 more
TL;DR: Wang et al. as discussed by the authors proposed stacking ensemble learning-based convolutional gated recurrent neural network (CGRNN) metamodel algorithm, which initially performs outlier detection to remove outlier data, using the Gaussian distribution method, and the Box-cox method is used to correctly order the dataset.
Journal ArticleDOI
Comparing Convolutional Neural Network and Machine Learning Models in Landslide Susceptibility Mapping: A Case Study in Wenchuan County
TL;DR: This research presented a model which was based on the CNN for LSM and methodically compare its capability with the traditional machine learning approaches, namely, support vector machine (SVM), logistic regression (LR), and random forest (RF).
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
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Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.