Prof. Dr. Naoshi Sakamoto, Tokyo Denki University, Japan
Naoshi Sakamoto holds M.S. and Dr.S. degree from Tokyo Institute of
Currently, he is a professor in Tokyo Denki University, Japan.
Since 1992, he had been an assistant researcher in Hitotsubashi University
and Tokyo Institute of Technology, successively.
He had engaged in constructing and managing the campus area network.
Originally, he was studying theoretical computer science.
He won IEICE excellent paper award in 1999 for the study of the amount
of information among initial conditions for distributed algorithms.
Since 2001, he has been an associate professor at Tokyo Denki University.
Then, since 2014, he has been a professor.
Since 2016, he has also been an deputy director of Office of Educational
Development for two years in the University.
Speech Title: Learning Complexity of Neural
Abstract: Recently, deep learning is developing
greatly. Particularly, the field of image
recognition and the field of machine translation
have been making remarkable progress.
Moreover, there are various applications in many
fields such as the field of information technology.
Now, many researchers can not ignore them.
A current deep neural network requires a lot of
input data, high spec computer, and long training
time. Then, it acquires better recognizing ability
than the human recognizing ability.
Now, when a neural network learns, how much does it
require computational power and training time? The
learning complexity of neural networks has been
studying for a long time. The speaker is introducing
the fundamental theories and recent results about
learning of neural networks.