## 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)

Access: http://www.ism.ac.jp/access/index_e.html

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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.

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