The 17th Statistical Machine Learning Seminar (2014.5.16)

The 17th Statistical Machine Learning Seminar

Date/time: May 16 (Fri) 15:00-
Place: Seminar Room 5 (3F, D313),
Institute of Statistical Mathematics (Tachikawa, Tokyo)


TITLE: Deep Learning: Theory, Algorithms, and Applications

by Professor Pierre Baldi (University of California, Irvine)

ABSTRACT: Learning is essential for building intelligent systems, whether
carbon-based or silicon-based ones.
Moreover these systems do not solve complex tasks in a single step but
rather go through multiple processing stages.
Hence the question of deep learning, how efficient learning can be
implemented in deep architectures.
This fundamental question not only impinges on problems of memory and
intelligence in the brain, but it is also at the forefront of current
machine learning research. In the last year alone, new performance
breakthroughs have been achieved by deep learning methods in applications
areas ranging from computer vision, to speech recognition, to natural
language understanding, to bioinformatics. This talk will provide a brief
overview of deep learning, from its biological origins to some of the latest
theoretical, algorithmic, and application results. Particular emphasis will
be given to the mathematical analysis of the dropout algorithm, a relatively
new randomization algorithm for deep learning, and the development of
learning methods–in the form of recursive neural
networks– for structured, variable-size, data, and their applications to
the problems of predicting the properties of small molecules and the
structure of proteins.

SHORT BIO: Pierre Baldi is Chancellor’s Professor in the Department of
Computer Science, Director of the Institute for Genomics and Bioinformatics,
and Associate Director of the Center for Machine Learning and Intelligent
Systems at the University of California, Irvine. He received his PHD degree
from the California Institute of Technology. His research work is at the
interface of the computational and life sciences, in particular the
application of artificial intelligence and statistical machine learning
methods to problems in chemoinformatics, genomics, systems biology, and
computational neuroscience.
He is credited with pioneering the use of Hidden Markov Models (HMMs),
graphical models, and recursive neural networks in bioinformatics.
Dr. Baldi has published four books and over 250 peer-reviewed research
articles with an H-index of 68. He is the recipient of the 1993 Lew Allen
Award at JPL, the 1999 Laurel Wilkening Faculty Innovation Award at UCI, a
2006 Microsoft Research Award, and the 2010 E. R. Caianiello Prize for
research in machine learning.
He is also am elected Fellow of the AAAS, AAAI, IEEE, ACM, and ISCB.