Self-supervised learning for visual recognition
Abstract: We are interested in learning visual representations that are discriminative for semantic image understanding tasks such as object classification, detection, and segmentation in images/videos. A common approach to obtain such features is to use supervised learning. However, this requires manual annotation of images, which is costly, ambiguous, and prone to errors. In contrast, self-supervised [...]
Policy Decomposition : Approximate Optimal Control with Suboptimality Estimates
Abstract: Owing to the curse of dimensionality, numerically computing global policies to optimal control problems for complex dynamical systems quickly becomes intractable. In consequence, a number of approximation methods have been developed. However, none of the current methods can quantify by how much the resulting control underperforms the elusive globally optimal solution. We propose Policy [...]