Learning Model Constraints for Structured Prediction KU Leuven
Structured output prediction based on discriminatively trained probabilistic graphical models is a powerful framework that has lead to a large improvement in predictive systems. These models, however, often require strong a priori constraints to guarantee tractable inference procedures. These constraints can limit the power of the model to provide good predictions, and can therefore be viewed as a necessary evil. This project will develop ...