A hybrid algorithm for learning sparse and linear discriminant sequences

A hybrid algorithm for learning sparse and linear discriminant sequences – Although the generalization error rates for a large class of sparse and linear discriminant sequences have not improved significantly, the number of samples is still increasing exponentially with increasing sample size. We present a novel method to estimate the variance, which is an important variable in many sparse and linear discriminant sequences. The goal is to estimate the variance directly via a variational approximation to the covariance matrix of the data, which can be viewed as a nonconvex optimization problem. We show that, by using a variant of the well-known nonconvex regret bound, we can construct a variational algorithm that can learn the $k$-norm of the covariance matrix with as few as $ninfty$ regularized regret. The proposed approach outperforms the conventional variational algorithm for sparse and linear discriminant sequences.

One of the important challenges in semantic parsing is the problem of automatically detecting the human-level semantic similarity between concepts. Recent work has shown that the visual saliency metric is a very useful feature for improving the human-level semantic similarity between concepts. In this paper, we propose a novel visual saliency metric for semantic parsing. Inspired by the visual saliency metric, we propose a novel convolutional neural network (CNN) with a deep learning model to learn the human-level semantic similarity in a visual context. To this end, we train a CNN to distinguish the semantic similarity between two examples. We report a high level semantic similarity (i.e. human-level similarity) between two examples in the semantic parsing task. Our experiments on multiple datasets reveal that our CNN can perform highly compared with state-of-the-art neural networks such as CNN-RNN and CNN-SVM. Our experiments show the superiority of our CNN in reducing human classification delays and the significant improvement compared with other CNN-based saliency metric.

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A hybrid algorithm for learning sparse and linear discriminant sequences

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  • Unsupervised learning of motion

    Boosting and Summarization of Human Parsing Templates Using Neural NetworksOne of the important challenges in semantic parsing is the problem of automatically detecting the human-level semantic similarity between concepts. Recent work has shown that the visual saliency metric is a very useful feature for improving the human-level semantic similarity between concepts. In this paper, we propose a novel visual saliency metric for semantic parsing. Inspired by the visual saliency metric, we propose a novel convolutional neural network (CNN) with a deep learning model to learn the human-level semantic similarity in a visual context. To this end, we train a CNN to distinguish the semantic similarity between two examples. We report a high level semantic similarity (i.e. human-level similarity) between two examples in the semantic parsing task. Our experiments on multiple datasets reveal that our CNN can perform highly compared with state-of-the-art neural networks such as CNN-RNN and CNN-SVM. Our experiments show the superiority of our CNN in reducing human classification delays and the significant improvement compared with other CNN-based saliency metric.


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