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Interesting papers from NIPS 2016 II: f-GAN Training samplers with minimal divergence

The Intelligence of Information

I will review today one other interesting paper from the last NIPS 2016 Conference. This time the paper that I’ve chosen is about a relatively new unsupervised  machine learning method implemented by a system of two neural networks called Generative Adversarial Networks (GAN). This is a technique that is being used with some success, specially in Computer Vision applications, but the authors of this paper claim possible applications within natural language processing and signal processing frameworks.

One aspect of this method that is somehow innovative is the interplay of two neural networks which are performing two opposed tasks in an adversarial zero-sum game setting. One would have thought this to be unproductive, but we should recall that this is only a methodological approach; this is intended to improve how training data in a machine learning framework helps the generation process of real test data in order for the respective algorithm…

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