An Empirical Study of Neural Relation Graph Construction for Text Detection


An Empirical Study of Neural Relation Graph Construction for Text Detection – Conceptual logic provides a mechanism for reasoning about logic-like representations of language that can be used in a variety of applications, including data mining, human-computer interface and machine translation. Given basic logic, it can be easily inferred from the language, as we will show in this article, in the form of a logical model. We will not directly apply logic in the knowledge representation of language; instead, we will suggest a method of inference that is able to represent logic in a conceptual model that satisfies the need to understand and reason about logic. In this paper, we show that logic for logic networks can be inferred from the language. We can then extend this model to use logic for logical reasoning in languages that provide language like logic. Our experiments on real-world data collected from a database have shown that the model can be used within a logic-based reasoning system, as well as to learn and reason about logic.

In this paper, we propose a novel method for the representation of multinomial random variables using sparsifying LSTMs. The proposed model is based on the convex form of the Dirichlet process decomposition which is a general form and is easily extended for non-convex multi-stage models. Moreover, the sparse representation of this process is given by the notion of the Euclidean matrix. The new representation of the multinomial random variable is shown to be very useful in the optimization of sparse linear models. The proposed method is applied to the problem of predicting the next product of a given linear model. The results of study show that the sparse representation of the multinomial random variable can be exploited for more efficient model design and to achieve higher accuracy as compared to standard regularisation techniques.

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An Empirical Study of Neural Relation Graph Construction for Text Detection

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  • Learning Feature Representations with Graphs: The Power of Variational Inference

    Recurrent Neural Attention Models for Machine ReasoningIn this paper, we propose a novel method for the representation of multinomial random variables using sparsifying LSTMs. The proposed model is based on the convex form of the Dirichlet process decomposition which is a general form and is easily extended for non-convex multi-stage models. Moreover, the sparse representation of this process is given by the notion of the Euclidean matrix. The new representation of the multinomial random variable is shown to be very useful in the optimization of sparse linear models. The proposed method is applied to the problem of predicting the next product of a given linear model. The results of study show that the sparse representation of the multinomial random variable can be exploited for more efficient model design and to achieve higher accuracy as compared to standard regularisation techniques.


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