… is a fully scalable model in terms of the number of vehicles. In other words, the model can
handle random number of vehicles and traffic levels without sacrificing prediction performance …

… others (with randomly sampled views … random aggregation method based on the deep
ConvNet classification approach; 3) we validate on three different datasets with different numbers …

… Random numbers evenly distributed from 30 N to 100 N Action position of hammering excitation
Random numbers … research on convolution neural network in random structural damage …

… Therefore, in order to propose a method to perform deep learning in a limited … the number
of parameters of the convolutional neural network, which is the base of the neural network. …

… In this paper, we introduce a robust lane detection method based on the combined convolutional
neural network (CNN) with random sample consensus (RANSAC) algorithm. At first, we …

… Our convolution neural network consists of two convolutional layers, maxpooling layers,
locally fully-connected layers, a DropConnect layer, and a final 2-way softmax layer for …

… compatibility with convolutional neural networks. Inspired by this property, we investigate
the combination of Markov random field models with deep convolutional neural networks for …

… Convolutional neural network (CNN) has demonstrated promising performance in
classification and prediction in various fields. In this study, a CNN is used for mineral prospectivity …

… It is important because for biometric data, the number of training face images per user are
limited and deep neural networks require a large number of training images to achieve good …

… quality of random sequence generation and to improve the randomness of random numbers
generated by PRNG, a PRNG with a feedback controller based on Hopfield neural network …