Over the past decade, deep learning has advanced a lot and proved to be useful for solving various problems. In this project seminar we research the use of deep learning approaches to solve combinatorial problems with some applications to computer vision. For example, in many computer vision problems, there is an underlying graph representation which allows for the development of deep learning algorithms to computer vision problems based on combinatorial graph optimizations.
Other combinatorial optimization problems focus on (but are not limited to) routing, solving NP-hard problems, optimizing deep neural networks, keypoints computation, studying the process of activation in the combinatorial setting, object detection, and optimizing deep learning frameworks that use combinatorial algorithms (such as nearest neighbours) as part of their computation.
In particular, we will work on selected topics in small groups. The goal is to develop novel applications of deep learning approaches. In the end, you will present your findings to each other as well as create a scientific report.