… phrase “convolutional neural network” in 05/2021), and IEEE (publications retrieved from
IEEE Xplore searching for the term “convolutional neural network” in full text only in 05/2021). …

… A convolutional neural network is a neural network that has one or more convolutional
layers… There will be architectures using convolutional layers which will not be called ‘convolutional …

… With the ever increasing application of Convolutional Neural Networks to … networks to
revised convolution layers. We offer our contribution to the latter and propose a novel convolution …

… convolutional neural networks (CNNs) to general graph-structured data by introducing a ‘diffusion-convolution…
the standard convolution operation, the diffusion-convolution operation …

… In this paper, we tried to analyze the properties of convolutional neural networks. A …
only a first step towards a full mathematical understanding of convolutional neural networks. …

… network architecture to be set up. This paper has outlined the basic concepts of Convolutional
Neural Networks, … detailing how best to structure the network in most image analysis tasks. …

… We have shown that supervised training of convolutional neural network can be used not
only for standard discriminative tasks, but also for generating images given highlevel class, …

… Multiple levels allow the network to learn distinct features. Deep learning has emerged as an
… such as deep belief networks, recurrent neural networks, convolution neural networks etc. …

… During the last decade, Convolutional Neural Networks (CNNs) have become the de facto
… CNNs are feed-forward Artificial Neural Networks (ANNs) with alternating convolutional and …

… convolutional neural networks (CNN), defined as deep, feed-forward ANN. Convolutional
neural networks extend classical ANN by adding more ‘depth’ into the network, as well as …