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Modeling using neural networks requires less formal statistical training.
We find that convolutional neural networks need significantly less training data to obtain the state-of-the-art performance than previously proposed methods.
Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task.
Recently, methods of tracking based on convolutional neural networks (CNNs) have been proposed [12, 13]; CNN methods have produced excellent results [14] but require a lot of data for training.
Second, they developed several deep neural network architectures including Convolutional Neural Networks (CNNs) using 1-D convolution with pooling, and Recurrent Neural Networks (RNNs).
The technology was powered by what he called "convolutional neural networks".
He was known for work involving something called convolutional neural networks, which he had developed for software that banks use to scan and read checks.
Region-based convolutional neural networks.
We use convolutional neural networks as our layered rich representation.
The deep learning method was the convolutional neural networks (CNN).
Google's secret for the speed of instant translations, is its use of convolutional neural networks.
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