Improving the Performance of Recurrent Neural Networks Using Unsupervised Learning

Improving the Performance of Recurrent Neural Networks Using Unsupervised Learning – Deep reinforcement learning (RL) has proven to be a successful approach for long-term reinforcement learning in both artificial and real-world settings. In RL, as previously described, the task of learning an action from a given input, will be learned using two tasks: i) to control the agent’s behavior, and ii) to control the agent’s reward. However, RL algorithms are usually linear in time, and it is not possible to solve those RL instances for all possible trajectories. In RL algorithms, a linear policy may not follow the trajectory for each possible trajectory. Therefore, learning an RL algorithm based on policy completion may not be feasible. In this paper, we propose a simple RL algorithm, named Replay, that learns the policy in RL algorithms. We compare the RL algorithm to several RL algorithms with linear policies for all possible trajectories of reward functions. Our algorithm outperforms them on several real-world datasets.

This paper presents a novel approach to the translation of word vectors for machine translation. To this end, we proposed a novel network-based approach for neural net translation. Unlike most previous work on neural net translation, this formulation directly addresses a problem specific to human performance on word-to-word translation in a language that does not use word vectors for training. In particular, we propose an efficient convolutional neural network (CNN) that trains a fully-defined vector representation of the input language. Additionally, we also present a method of embedding the output image into a vector, and use a discriminative feature learning procedure to classify the features in such a way as to enable a good translation capability. Experiments on two standard datasets demonstrate that the proposed CNN is a very effective tool for translation for the problem of translating natural language sentences to the language of the given language. The proposed CNN provides a baseline for our work, and also has a better understanding of the performance of the models than all other approaches.

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Improving the Performance of Recurrent Neural Networks Using Unsupervised Learning

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    AnalogNet: A Deep Neural Network Training Resource Based Machine Learning Tool for Real World BankingsThis paper presents a novel approach to the translation of word vectors for machine translation. To this end, we proposed a novel network-based approach for neural net translation. Unlike most previous work on neural net translation, this formulation directly addresses a problem specific to human performance on word-to-word translation in a language that does not use word vectors for training. In particular, we propose an efficient convolutional neural network (CNN) that trains a fully-defined vector representation of the input language. Additionally, we also present a method of embedding the output image into a vector, and use a discriminative feature learning procedure to classify the features in such a way as to enable a good translation capability. Experiments on two standard datasets demonstrate that the proposed CNN is a very effective tool for translation for the problem of translating natural language sentences to the language of the given language. The proposed CNN provides a baseline for our work, and also has a better understanding of the performance of the models than all other approaches.


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