… 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 …

… 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 …

… 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 …

… • 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 dataset used in this work is called TUT-SED Synthetic 2016. It is a publicly available
polyphonic SED dataset, which consists of synthetic mixtures created by mixing isolated sound …

… 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 …

… has been increasing interests in deep neural network models for solving various … Neural
Networks (CNN) architecture,24 which is a bio-inspired hierarchical multilayered neural network …

… Neural network was used to classify small patches of size 9x9 and it reached 86.88%
accuracy. In 2016, International Symposium on Biomedical Imaging (ISBI) held the grand …

… 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 …

… This work presents a new convolution neural network architecture that uses feature information
from facial parts (Eyes and Mouth) as input into two separate CNN channels. The output …