Formal Techniques for Neural-symbolic Modeling

About

This is intended to be an advanced course on current methods for combining symbolic logic and neural networks, with applications to problems in natural language processing (NLP). In particular, we focus on techniques that use symbolic knowledge and declarative constraints to train machine learning models by compiling the corresponding symbolic logic into a differentiable form, also known as the logic as a loss function family of approaches. Details of current approach in NLP, as well as the formal and algorithmic techniques needed to doing this, will be covered in detail and drawn from the broader literature of neural-symbolic learning and reasoning.

Course leader

Kyle Richardson and Vivek Srikumar

Target group

Students

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