Deep Learning: Theory, Algorithms, and Applications

NII Shonan Meeting:

@ Shonan Village Center, May 19-22, 2014

NII Shonan Meeting Report (ISSN 2186-7437):No.2014-5

Organizers

  • Pierre Baldi, University of California in Irvine, USA
  • Kenji Fukumizu, Institute of Statistical Mathematics, Japan
  • Tomaso Poggio, Massachusetts Institute of Technology, USA

Overview040_Group Photo

The ability to learn is essential to the survival and robustness of biological systems. There is also growing evidence that learning is essential to build robust artificial intelligent systems and solve complex problems in most application domains. Indeed, one of the success stories in computer science over the past three decades has been the emergence of machine learning and data mining algorithms as tools for solving large-scale problems in a variety of domains such as text analysis, computer vision, robotics, and bioinformatics. However, we are still far from having a complete understanding of machine learning and its role in AI, and plenty of challenges, both theoretical and practical, remain to be addressed.

Complex problems cannot be solved in one single step and often require multiple processing stages in both natural and artificial systems. For instance, visual recognition in humans is not an instantaneous process and requires activation of a hierarchy of processing stages and pathways. The same is true for all the best performing computer vision systems available today. Thus deep learning architectures, comprising multiple, adaptable, processing layers are important for the understanding and design of both natural and artificial systems and, today, are at the forefront of machine learning research. In the past year alone, deep architectures and deep learning have achieved state-of-the-art performance in many application areas ranging from computer vision, to speech recognition, to bioinformatics.

It is this recent wave of progress that provides the relevant context for the proposed meeting which will focus on all aspects of deep architectures and deep learning, with a particular emphasis on understanding fundamental principles because there is still very little theoretical understanding of deep learning, in spite of the recent progress. Thus a major thrust of the meeting will be to foster theoretical analyses of deep learning. In addition to theory, topics to be covered will include also algorithms and applications. The primary intellectual focus of the meeting will be on deep learning in artificial systems. However, deep learning draws some of its inspiration from, and has close connections to, neuroscience. Thus presentations and discussions bridging learning in natural and artificial learning systems will also be encouraged.

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