… Micheli [55] proposed the first graph convolution neural network based on spatial domain,
called neural network for graphs (NN4G). It performs convolutions by accumulating a neighbor …

… of deep neural networks, convolutional neural networks have … units, the research on
convolutional neural networks has been … applications of convolutional neural networks in …

… models is convolutional neural network (CNN), a class of artificial neural networks that has
… Other deep learning models, such as recurrent neural networks for sequence models, are …

… This is a note that describes how a Convolutional Neural Network (CNN) operates from a …
The Convolutional Neural Network (CNN) has shown excellent performance in many …

… using neural network starting from linear classifiers. Then, Chap. 3 shows how to derive
convolutional neural networks from fully connected neural networks. It also reviews classical …

… In this work, the authors use facial landmarks detected by a camera as input to a convolutional
neural network to categorize tiredness instead of a multi-layer perceptron. The suggested …

… layers of a neural network with the convolution operation have less capacity than that of a
fully connected network. That is, there exists datasets that a fully connected network will be …

… neural network is the convolution operation. It has made … Moreover, convolutional neural
network has received … introduces the convolution operation of the convolutional neural network. …

… convolution technique. In the first experiment set utilization of the differential convolution on
a traditional convolutional neural network … set differential convolution adaptation raised the …

… We developed a 9-layer deep convolutional neural network (CNN) to automatically identify
5 different categories of heartbeats in ECG signals. Our experiment was conducted in original …