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

Projects

Interactive Deep Learning for Image Labeling and Analysis

Deep learning algorithms have shown to be successful in applications such as speech recognition and computer vision. A key component ...
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