Fast learning rates and the effectiveness of adversarial reinforcement learning for dialogue policy computation


Fast learning rates and the effectiveness of adversarial reinforcement learning for dialogue policy computation – This paper presents a new technique for learning deep models from noisy data by learning deep neural networks trained in the belief, prior and feedback representations. This technique is based on a novel technique, the Recurrent Neural Net (RMN), and based on the combination of multiple layers of networks trained jointly with one or more hidden layers. Experiments obtained using the dataset MNIST show that the RMN can learn to predict the posterior probability distribution of labels given similar data, outperforming a baseline CNN trained to generate positive labels with good accuracy. A comparison of the RMN model with other state-of-the-art models on the MNIST dataset shows that the RMN outperforms the model trained in the prior representation.

This paper proposes an image recognition method that employs a hierarchical representation for categorization and segmentation in order to reduce the number of features for training and segmentation. We proposed a fully Convolutional neural network with a stacked architecture built specifically for classification and segmentation. The structure of this new architecture is described in terms of an evolutionary algorithm with no explicit feature selection or feature tracking. To validate the performance of the proposed method, a detailed analysis of the hierarchical representation with features from different cameras is presented. The proposed architecture can be viewed as an extension of the convolutional neural network architecture, as we show that it does not have any loss or loss with respect to a deep network. Using the new architecture our method performs a high accuracy classification task in ImageNet (7.2%) while performing at least 20.8% accuracy on the classification task as compared to the baseline.

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Fast learning rates and the effectiveness of adversarial reinforcement learning for dialogue policy computation

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  • The Impact of Randomization on the Efficiency of Neural Sequence Classification

    Stacked Extraction and Characterization of Object Categories from Camera ResidualsThis paper proposes an image recognition method that employs a hierarchical representation for categorization and segmentation in order to reduce the number of features for training and segmentation. We proposed a fully Convolutional neural network with a stacked architecture built specifically for classification and segmentation. The structure of this new architecture is described in terms of an evolutionary algorithm with no explicit feature selection or feature tracking. To validate the performance of the proposed method, a detailed analysis of the hierarchical representation with features from different cameras is presented. The proposed architecture can be viewed as an extension of the convolutional neural network architecture, as we show that it does not have any loss or loss with respect to a deep network. Using the new architecture our method performs a high accuracy classification task in ImageNet (7.2%) while performing at least 20.8% accuracy on the classification task as compared to the baseline.


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