An Empirical Study of Neural Relation Graph Construction for Text Detection


An Empirical Study of Neural Relation Graph Construction for Text Detection – In this work, we focus on the task of semantic prediction using semantic representations of text. Our main goal is to develop a learning algorithm for using semantic representations. We present two neural networks. One of the two networks aims at finding the semantic representation that best matches the text that is being given. The other network also aims at finding the semantic representation that has the highest relevance for the given text. The purpose of our neural network is to extract features that are related to the text in a high dimensional representation, and then use them to train our model. In this work, we show that the features learned using semantic representations, obtained by our neural network, are more informative than the features obtained by a conventional semantic prediction approach.

In this paper, we propose a novel framework for a deep neural network (DNN) architecture which operates on sparsely structured representations of a data set, in the form of a multi-step learning algorithm. The main contributions of the proposed framework are: 1) the method of learning the sparse representation is to learn a discriminative model that uses the data from a deep network. 2) The system is designed to be robust and efficient to unknown data. 3) The system performs in terms of the time and space required to learn the feature vectors of the data for the learning process, which can be computed by sampling the whole model. 4) The model performs in terms of the number of features, and the number of features that can be learned, which are necessary for learning the feature vectors. Furthermore the system can be used to provide a new deep learning algorithm for the system. We also compare the performance of the proposed framework to existing methods.

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An Empirical Study of Neural Relation Graph Construction for Text Detection

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  • Examining Kernel Programs Using Naive Bayes

    A Stochastic Approach to Deep LearningIn this paper, we propose a novel framework for a deep neural network (DNN) architecture which operates on sparsely structured representations of a data set, in the form of a multi-step learning algorithm. The main contributions of the proposed framework are: 1) the method of learning the sparse representation is to learn a discriminative model that uses the data from a deep network. 2) The system is designed to be robust and efficient to unknown data. 3) The system performs in terms of the time and space required to learn the feature vectors of the data for the learning process, which can be computed by sampling the whole model. 4) The model performs in terms of the number of features, and the number of features that can be learned, which are necessary for learning the feature vectors. Furthermore the system can be used to provide a new deep learning algorithm for the system. We also compare the performance of the proposed framework to existing methods.


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