Deep Learning through Sparse and Low-Rank Modeling

Preface

Deep learning has achieved tremendous success in various applications of machine learning, data analytics, and computer vision. It is easy to be parallelized with a low inference complexity and could be jointly tuned in an end-to-end manner. However, generic deep architectures, often referred to as “black-box” methods, largely ignore the problem-specific formulations and domain knowledge. They rely on stacking somewhat ad-hoc modules, which makes it prohibitive to interpret their working mechanisms. Despite a few hypotheses and intuitions, it is widely recognized as difficult to understand why deep models work, and how they can be related to classical machine learning models. On the other hand, sparsity and low rankness are well ...