An Overview of Deep-Structured Learning for Information Processing

  • Li Deng

Proc. Asian-Pacific Signal & Information Proc. Annual Summit & Conference (APSIPA-ASC) |

View Publication

In this paper, I will introduce to the APSIPA audience an emerging area of machine learning, deep-structured learning. It refers to a class of machine learning techniques, developed mostly since 2006, where many layers of information processing stages in hierarchical architectures are exploited for pattern classification and for unsupervised feature learning. First, the brief history of deep learning is discussed. Then, I develop a classificatory scheme to analyze and summarize major work reported in the deep learning literature. Using this scheme, I provide a taxonomy-oriented survey on the existing deep architectures, and categorize them into three types: generative, discriminative, and hybrid. Two prime deep architectures, one hybrid and one discriminative, are presented in detail. Finally, selected applications of deep learning are reviewed in broad areas of information processing including audio/speech, image/video, multimodality, language modeling, natural language processing, and information retrieval.