Richard Wang - generative adversarial nets (GANs)
Wei (Richard) Wang will be introducing generative adversarial nets (GANs) - a deep learning model that has various and often surprising applications in computer vision and natural language processing. He will explain his recent work on improving the training of GANs for image generation tasks (accepted to ICLR 2019).
Richard recently received his Ph.D. degree from the University of Melbourne where he worked on several generative models: principal component analysis, Gaussian mixture models and generative adversarial nets.
Mat Kelcey - Practical Learning to Learn
Mat Kelcey is a machine learning consultant at ThoughtWorks, and will be
discussing some of the core concepts of gradient descent when training a model on a single large dataset. His talk will also discuss a couple of methods, using the same concepts, to train models from a large number of small, but related, datasets.