Representation Learning

This research theme aims to learn good feature representations from raw sensory data. Developing suitable representations is the key to achive a desired learning system, that either generalizes well to new data and is ideally capable of describing the complicated data to the user with the help of human-interpretable representations. The process of learning representations can be knowledge-driven or data-driven, be learned from unlabeled or labeled data, and be with or without human interaction. The representations can be on different levels of abstraction to facilitate bridging the gap between low-level sensory data and high-level abstract concepts.

Keywords:

deep learning
neural network models
probabilistic graphical models
interactive learning
interpretable representation
multi-level abstraction