… The outputs of the last layer of the CNN are then input to a fully connected network, which
is called the classification layer. A feedforward neural network is used as a classifier in this …

… In recent years, deep learning with regular and tightlycoupled arrays of sigmoidal neurons
has come to the forefront as a possible way to overcome the limitations of neural networks …

… • Instead of using hand-crafted features, we have explored the ability of convolutional
neural network (CNN) to improve the classification performance. • The proposed approach is …

… The convolutional neural network (CNN) is a subset of the neural network (NN) embedding
a convolution structure of small receptive fields (Figure 1). The layers of convolution …

… In this work, we use deep neural networks in general and convolutional neural networks in
… A schematic representation of a one-dimensional convolutional neural network is shown in …

… In this paper, we not only illustrate the parallels between digital spiking neural networks and
… applications for networks of neurosynaptic cores,” in Neural Networks (IJCNN), 2013 Int. …

… ward neural networks and deep convolutional neural networks … We use the first layer of a
deep neural network to learn a … constrained deep convolutional neural network. The proposed …

… Abstract—The field of artificial neural networks has a long history of several decades, where
the theoretical contributions have progressed with advances in terms of power and memory …

… We present a deep learning approach, that combines three Convolutional Neural Networks
to detect people at different scales, which is the first time that a multi resolution model is …

… We trained a deep all-convolutional neural network with masked global pooling to perform …
audio tagging in the DCASE-2016 contest. Our network achieved an average accuracy of 84.5…