A Unified Deep Architecture for Structured Prediction


A Unified Deep Architecture for Structured Prediction – Deep reinforcement learning (DRL) has been successfully applied to the task of predicting the health of a human being. In this article, a DRL approach is proposed to perform reinforcement learning (RL). The learning objective for a RL system consists of finding optimal strategies for a given task, and is formulated as a multi-task learning problem. This can be represented by a set of reinforcement learning algorithms, and can be solved by different reinforcement learning algorithms that learn to minimize the variance in the output of the RL algorithm. Several different reinforcement learning algorithms are used for learning to model the current state of the RL. The RL algorithms of this work are implemented in the framework of a two-stage neural network architecture (NN), in which the RL algorithm is modified via learning to learn new policies. Experimental results conducted on a real-world dataset, with a number of simulated instances, illustrate the superior generalization performance of the proposed RL-SRNN architecture compared to the traditional RL algorithms.

We propose a novel framework for learning an intuitive and scalable representation of text. We show how to build text representations of semantic sentences from a text representation of the words. We show how to use the learned representation to infer the source sentence, the text sentence and their relation to each other, a sequence of sentences of each word, and the corresponding semantic text that could be spoken. This represents a significant step towards achieving a universal language representation which can translate sentences in a rich language into sentences in less-complex language.

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A Unified Deep Architecture for Structured Prediction

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  • A Unified Collaborative Strategy for Data Analysis and Feature Extraction

    Towards a Theory of Neural Style TransferWe propose a novel framework for learning an intuitive and scalable representation of text. We show how to build text representations of semantic sentences from a text representation of the words. We show how to use the learned representation to infer the source sentence, the text sentence and their relation to each other, a sequence of sentences of each word, and the corresponding semantic text that could be spoken. This represents a significant step towards achieving a universal language representation which can translate sentences in a rich language into sentences in less-complex language.


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