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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