Towards Theoretical Foundations of Unsupervised Deep Learning

PI: Aleksander Madry, Department of Electrical Engineering & Computer Science, MIT

Abstarct: Generative adversarial networks (GANs) are a general approach to tackling unsupervised learning tasks using deep neural networks. This approach is widely used and very successful in practice, but not really understood from theoretical point of view. This proposal aims to remedy that problem by developing theoretical foundations for studying GANs and, more broadly, unsupervised deep learning. Specifically, the PI will build a rigorousand principled framework for unsupervised deep learning using GANs, and analyze its expressive power and complexity. The resulting understanding will then be used to guide the design of new, improved methods for training GANs. These methods, and the heuristics they inspire, will be tested experimentally. 

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