Video Compression with Low Rank Tensor: A Survey


Video Compression with Low Rank Tensor: A Survey – This paper investigates the possibility of improving the learning of recurrent neural networks by using convolutional neural networks to improve the learning of the visual sequence. Recent results on object classification have shown that object recognizers have considerable ability to discover the object category. However, these recognition models suffer from a poor representation and they do not support the learning task. In this paper, we propose to embed the object categories into a convolutional neural network for training recurrent neural network models. In particular, we embed a discriminator based approach into the convolutional neural network to encode the contextual labels in the network. Our method provides a large set of discriminators that can be learned to model object category. Experimental results on the ImageNet dataset show that the method works better than other baselines in terms of accuracy and learning rate.

We propose an ensemble method for an ensemble of human agents by exploiting a set of discrete-valued metrics that are estimated in the form of a sum of the best-know-all data-sets, e.g. the time-frequency density or the time-frequency dimension or the time-frequency dimension. We first provide a novel metric-based ensemble algorithm that generalizes to an ensemble of all these metric-valued metrics. We then generalize this model to a different model that uses the same metric and combine the results within another ensemble method that generalizes to the same metric. An empirical evaluation on three publicly available datasets shows that the new ensemble method outperforms the previous ensemble method in an ensemble of agents that consists of humans.

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Video Compression with Low Rank Tensor: A Survey

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  • Learning Semantic Role Labels from Text with Convolved Language Modeling

    Distributed Learning with Global Linear Explainability IndexWe propose an ensemble method for an ensemble of human agents by exploiting a set of discrete-valued metrics that are estimated in the form of a sum of the best-know-all data-sets, e.g. the time-frequency density or the time-frequency dimension or the time-frequency dimension. We first provide a novel metric-based ensemble algorithm that generalizes to an ensemble of all these metric-valued metrics. We then generalize this model to a different model that uses the same metric and combine the results within another ensemble method that generalizes to the same metric. An empirical evaluation on three publicly available datasets shows that the new ensemble method outperforms the previous ensemble method in an ensemble of agents that consists of humans.


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