Efficient Video Super-resolution via Finite Element Removal


Efficient Video Super-resolution via Finite Element Removal – We present the state-of-the-art ConvNet framework for video super resolution. The proposed framework is able to handle large datasets and challenging scenes. To make it practical, we show that the proposed framework can be optimized for a wide range of visual domains and is not limited in its scope. Experiments show that the proposed framework outperforms other state-of-the-art methods on several video datasets.

Learning nonlinear graphical models is a fundamental approach to many real-world applications. In this paper, we propose an efficient method for learning such a powerful learning algorithm under uncertainty. The learning algorithm is then used to obtain accurate and accurate regression probabilities for various nonlinear graphical model configurations. We demonstrate the effectiveness of our algorithm using datasets of 20,000 users. Our algorithm achieves a significant boost in accuracy, and gives a comparable number of false positive and false negative results compared to previous works. Besides the use of nonlinear graphical models, our algorithm has the advantage of being easy to train for data of arbitrary size. We demonstrate that our algorithm is able to achieve good results with a smaller training set than previous models: it is faster to train, and is able to accurately predict the data of interest.

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Efficient Video Super-resolution via Finite Element Removal

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  • Deep Learning for Data Embedded Systems: A Review

    Binary LSH Kernel and Kronecker-factored Transform for Stochastic Monomial Latent Variable ModelsLearning nonlinear graphical models is a fundamental approach to many real-world applications. In this paper, we propose an efficient method for learning such a powerful learning algorithm under uncertainty. The learning algorithm is then used to obtain accurate and accurate regression probabilities for various nonlinear graphical model configurations. We demonstrate the effectiveness of our algorithm using datasets of 20,000 users. Our algorithm achieves a significant boost in accuracy, and gives a comparable number of false positive and false negative results compared to previous works. Besides the use of nonlinear graphical models, our algorithm has the advantage of being easy to train for data of arbitrary size. We demonstrate that our algorithm is able to achieve good results with a smaller training set than previous models: it is faster to train, and is able to accurately predict the data of interest.


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