Thursday, 29 March 2018, 15:00 at MR14, Centre for Mathematical Sciences – Generative Adversarial Networks, a.k.a. GANs, have found great applicability in machine learning in applications ranging from image synthesis to compressed sensing, domain adaptation and super-resolution. They are defined by setting up a two-player zero-sum game between two neural networks, trained using gradient descent on samples from a target distribution. Despite …