Hybrid AI integrating knowledge representation with deep learning

Deep learning techniques have shown great success in many applications, such as computer vision and natural language processing. However, in many cases purely data-driven approaches would provide suboptimal results especially when limited data are available for training the models. This dependency on large-scale training data is well understood as the main limitation of deep learning models.

One way to mitigate this problem is to incorporate knowledge priors into the model, similarly to how humans reason with data; and there are various types of knowledge priors, such as data-specific relational information, knowledge graphs, logic rules and statistical modelling.

In this PhD project, we will investigate novel methods that effectively integrate knowledge priors and commonsense reasoning with deep learning models. Such models can be developed for a wide range of application domains, such as computer vision, social networks, biological discovery and human-robot interaction.

How to Apply

Express your interest in this project by emailing Associate Professor Yang Song. Include a copy of your CV and your academic transcript(s).