Joint Learning of Neural Networks

The recent advances in deep learning bring many opportunities and challenges to apply this technique into some special areas, such as healthcare. This thesis focuses on developing new joint learning methods to address three fundamental challenges in deep learning, including data limitation, model over-parameterization, and model uncertainty. By addressing these challenges, we can obtain better neural models to build more efficient automatic diganosis systems.