The paper studies the problem of training a neural network to perform a given task in a setting where the training data is noisy. The authors propose a method that uses a generative model to learn a distribution over the noisy data, and then uses this distribution to train the neural network. The method is shown to outperform existing methods on a variety of tasks.
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