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.

We consider the learning problem of learning a continuous variable over non-negative vectors from both the data representation and the distribution of a set of variables. In this paper, we propose a novel technique for learning a continuous variable over arbitrary non-negative vectors, using any non-negative vector as input and learning a linear function from their representations of the set of vectors. The solution obtained depends on the number of variables, the sparsity of the vector, and the number of the variables. The approach is based on a nonconvex objective function and an upper bound, using simple iterative solvers. The method is fast and has low computational cost. As such, it is a promising approach in practice.

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

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  • Using Natural Language Processing for Analytical Dialogues

    Stochastic Conditional Gradient for Graphical Models With Side InformationWe consider the learning problem of learning a continuous variable over non-negative vectors from both the data representation and the distribution of a set of variables. In this paper, we propose a novel technique for learning a continuous variable over arbitrary non-negative vectors, using any non-negative vector as input and learning a linear function from their representations of the set of vectors. The solution obtained depends on the number of variables, the sparsity of the vector, and the number of the variables. The approach is based on a nonconvex objective function and an upper bound, using simple iterative solvers. The method is fast and has low computational cost. As such, it is a promising approach in practice.


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