Dataset augmentation, the practice of applying a wide array of domain-specific transformations to synthetically expand a training set, is a standard tool in supervised learning. While effective in tasks such as visual recognition, the set of transformations must be carefully designed, implemented, and tested for every new domain, limiting its re-use and generality. In this talk, I will describe two recent efforts which transform data not in input space, but in a feature space found by unsupervised learning. The first is motivated by evidence that people mentally simulate transformations in space while comparing examples, so-called "mental rotation". We employ a model that learns relations between inputs rather than the inputs themselves. This "relational" model actively transforms pairs of examples so that they are maximally similar in some feature space while respecting the learned transformational constraints. The second effort takes a more direct approach to domain-agnostic dataset augmentation. We start with data points mapped to a learned feature space and apply simple transformations such as adding noise, interpolating, or extrapolating between them. Working in the space of context vectors generated by sequence-to-sequence recurrent neural networks, this simple technique is demonstrated to be effective for both static and sequential data.

BIO: Graham Taylor received his PhD in Computer Science from the University of Toronto in 2009, where he was advised by Geoffrey Hinton and Sam Roweis. He spent two years as a postdoc at the Courant Institute of Mathematical Sciences, New York University working with Chris Bregler, Rob Fergus, and Yann LeCun. In 2012, he joined the School of Engineering at the University of Guelph as an Assistant Professor where he leads the Machine Learning Research Group.

Dr. Taylor's research focuses on statistical machine learning, with an emphasis on deep learning and sequential data. Much of his work has focused on “seeing people” in images and video, for example, activity and gesture recognition, pose estimation, emotion recognition, and biometrics.