On the Performance of Convolutional Neural Networks in Real-Time Resource Sharing Problems using Global Mean Field Theory


On the Performance of Convolutional Neural Networks in Real-Time Resource Sharing Problems using Global Mean Field Theory – In this paper, we propose a novel generalisation of the sparse regression problem for multiple regression. The problem is formulated as an optimisation problem in which the objective is to predict the number of variables in a data set. For data sets with a large number of variables, a sparse regression method can be applied. It can be used as a substitute to the sparse regression problem to obtain a low-dimension sparse predictor which can be used to predict the data. The solution to this problem is described using a variational Bayes estimator and a Gaussian mixture model. A maximum likelihood Bayes estimator is derived for each dimension. The resulting method is compared to the sparse regression algorithms, which have been shown to improve the accuracy and comparability of Bayes estimators both for variable prediction and for multiple regression. The experimental results revealed that these methods outperform the rest of the existing methods.

Deep convolutional neural networks (CNNs) are very powerful machine learning frameworks. In this work, we propose a fully convolutional CNN for unsupervised video classification, by means of preprocessed feature maps. We build models to learn new features and learn a recurrent model to learn the learned features. We demonstrate that the proposed CNN network can significantly cut in the number of features and learn new categories in fewer iterations than Convolutional Neural Networks (CNNs). In particular, the proposed CNN model reduces the model’s computational complexity and hence allows the CNNs to learn a new semantic representation. Our experiments on the MNIST dataset demonstrate that the performance of our proposed CNN is superior to CNN models.

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On the Performance of Convolutional Neural Networks in Real-Time Resource Sharing Problems using Global Mean Field Theory

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  • Improving Human Parsing by Exploiting Minimal Metric Accuracy in Deep Neural Networks

    Deep Learning for Fine-Grained Human Video Classification with Learned Features and Gradient DescentDeep convolutional neural networks (CNNs) are very powerful machine learning frameworks. In this work, we propose a fully convolutional CNN for unsupervised video classification, by means of preprocessed feature maps. We build models to learn new features and learn a recurrent model to learn the learned features. We demonstrate that the proposed CNN network can significantly cut in the number of features and learn new categories in fewer iterations than Convolutional Neural Networks (CNNs). In particular, the proposed CNN model reduces the model’s computational complexity and hence allows the CNNs to learn a new semantic representation. Our experiments on the MNIST dataset demonstrate that the performance of our proposed CNN is superior to CNN models.


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