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 Learning is not for human-assisted autonomous systems. However, the proposed approach offers a simple yet effective algorithm to automatically synthesize the objects within a neural network, which can be used during an autonomous system’s training.

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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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  • Stochastic gradient descent

    A Deep Knowledge Based Approach to Safely Embedding Neural NetworksDeep Learning is not for human-assisted autonomous systems. However, the proposed approach offers a simple yet effective algorithm to automatically synthesize the objects within a neural network, which can be used during an autonomous system’s training.


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