<p>The shape of each layer's output is given in parentheses, where N is the number of bases in the sequence. "Conv1" and "Conv7" indicate single base convolution and seven base convolution layers, as described in the text. "Dense" indicates a dense (fully connected) layer, where every output depends on every input.</p
<p>(A) Representations of the four major types of network topology. Nodes with the same color are me...
The layer/node structure of the CDRP deep learning architecture. On the left side: the CDRP-A autoen...
<p>The two left layers are for the convolution process and the two right layers are for linear conne...
The architecture of the convolutional neural network with corresponding kernel size (k), number of f...
<p>CNN-1 is composed of three layers, two convolutional layers and an output layer.</p
The structure of the convolutional neural network is displayed. Initially, two convolution and max p...
Each rectangle represents a layer in the network. Where appropriate the layer type is abbreviated (i...
It consists of three convolutional layers with max pooling applied at each layer, along with two ful...
<p>The architecture consists of one input layer, three convolutional layers, two max-pooling layers,...
By specifying the number of nodes in the input layer, hidden layer and output layer and selecting th...
A key challenge in designing convolutional network models is sizing them appro-priately. Many factor...
<p>The bottom layer shows the communication network, the middle layer represents the transportation ...
The left part of the figure shows down-sampling operation and right part shows up-sampling process. ...
Top network displays main architecture of the model, but better readability, several groups of convo...
(left) The density for each layer was calculated as the ratio of the sum of the weights of all conne...
<p>(A) Representations of the four major types of network topology. Nodes with the same color are me...
The layer/node structure of the CDRP deep learning architecture. On the left side: the CDRP-A autoen...
<p>The two left layers are for the convolution process and the two right layers are for linear conne...
The architecture of the convolutional neural network with corresponding kernel size (k), number of f...
<p>CNN-1 is composed of three layers, two convolutional layers and an output layer.</p
The structure of the convolutional neural network is displayed. Initially, two convolution and max p...
Each rectangle represents a layer in the network. Where appropriate the layer type is abbreviated (i...
It consists of three convolutional layers with max pooling applied at each layer, along with two ful...
<p>The architecture consists of one input layer, three convolutional layers, two max-pooling layers,...
By specifying the number of nodes in the input layer, hidden layer and output layer and selecting th...
A key challenge in designing convolutional network models is sizing them appro-priately. Many factor...
<p>The bottom layer shows the communication network, the middle layer represents the transportation ...
The left part of the figure shows down-sampling operation and right part shows up-sampling process. ...
Top network displays main architecture of the model, but better readability, several groups of convo...
(left) The density for each layer was calculated as the ratio of the sum of the weights of all conne...
<p>(A) Representations of the four major types of network topology. Nodes with the same color are me...
The layer/node structure of the CDRP deep learning architecture. On the left side: the CDRP-A autoen...
<p>The two left layers are for the convolution process and the two right layers are for linear conne...
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