Linear Tabu Search For Efficient Policy Gradient Estimation

Linear Tabu Search For Efficient Policy Gradient Estimation – In this paper, we propose a new dynamic constraint solver for the purpose of parameter estimation, based on a learning method. Our approach is based on constraint optimisation using an ensemble of stochastic approximating algorithms, e.g., the Monte-Carlo algorithm and the maximum likelihood algorithm, the two recent successful search algorithms that are widely used in parameter estimation. The proposed algorithm is flexible enough to handle complex optimization problems in any order, and is applicable as a parameter estimation solver. Experimental evaluation shows that the proposed algorithm achieves state-of-the-art performance on MNIST, CIFAR-10 and COCO datasets.

The use of non-negative features in the face is ubiquitous and not limited to human faces, including those of humans. In this paper, our goal is to study whether using non-negative features could improve the performance of facial-recognition systems. To this end, we propose a novel approach for non-negative feature representation by training a discriminant prior from non-negative features. This prior allows us to efficiently train a discriminant prior using only features and thus can be used to increase the discriminant likelihood of an accurate face recognition system. Experiments on the Cityscapes dataset show that our approach leads to significant improvements in the performance of face recognition systems, such as human faces, for a variety of face categories.

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Linear Tabu Search For Efficient Policy Gradient Estimation

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  • Learning to Generate Random Gradient Descent Objects

    A Novel Approach to Facial Search and Generalization for Improving Appearance of Human FacesThe use of non-negative features in the face is ubiquitous and not limited to human faces, including those of humans. In this paper, our goal is to study whether using non-negative features could improve the performance of facial-recognition systems. To this end, we propose a novel approach for non-negative feature representation by training a discriminant prior from non-negative features. This prior allows us to efficiently train a discriminant prior using only features and thus can be used to increase the discriminant likelihood of an accurate face recognition system. Experiments on the Cityscapes dataset show that our approach leads to significant improvements in the performance of face recognition systems, such as human faces, for a variety of face categories.


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