Training the Recurrent Neural Network with Conditional Generative Adversarial Networks


Training the Recurrent Neural Network with Conditional Generative Adversarial Networks – In this paper, We propose a novel, scalable and efficient method for learning sparse recurrent encoder-decoder networks. Building on the deep-learning framework of deep neural networks, our method combines the advantages of a deep-learning framework and recurrent encoder-decoder networks for learning the sparse encoder-decoder network, and shows promising results. Our method is fully scalable to handle many recurrent encoder-decoder networks, and achieves state-of-the-art results on both synthetic and real datasets.

Many machine learning applications are designed to handle small samples, in order to reduce the variance in the prediction model in the context of a large training set. The goal is to estimate the model’s predictive ability by means of the prediction metric defined as a pair of features of the same data pair, and to estimate the metric by means of a linear combination of these two features. In this work, we provide a novel method for estimating the metric in a deep learning setting, which we call ResNet-1. ResNet-1 is trained as a deep neural network to predict a single-label classification task for one of a large training set. It is trained using a large vocabulary of labeled data samples collected from a machine-learning classifier, whose predictions are aggregated as inputs, and then trained to predict the label distributions corresponding to the labeled data samples. Experiments on MS-COCO, CIMBA, and the large-scale MNIST dataset show that ResNet-1 consistently outperforms the trained deep learning model for predicting label distributions.

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Training the Recurrent Neural Network with Conditional Generative Adversarial Networks

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  • Semi-Automatic Construction of Large-Scale Data Sets for Robust Online Pricing

    Fast, Accurate Metric LearningMany machine learning applications are designed to handle small samples, in order to reduce the variance in the prediction model in the context of a large training set. The goal is to estimate the model’s predictive ability by means of the prediction metric defined as a pair of features of the same data pair, and to estimate the metric by means of a linear combination of these two features. In this work, we provide a novel method for estimating the metric in a deep learning setting, which we call ResNet-1. ResNet-1 is trained as a deep neural network to predict a single-label classification task for one of a large training set. It is trained using a large vocabulary of labeled data samples collected from a machine-learning classifier, whose predictions are aggregated as inputs, and then trained to predict the label distributions corresponding to the labeled data samples. Experiments on MS-COCO, CIMBA, and the large-scale MNIST dataset show that ResNet-1 consistently outperforms the trained deep learning model for predicting label distributions.


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