Bernoulli-RIKEN Symposium on Neural Networks and Learning

[Shun-ichi Amari]

Now we are accepting registration for the
Bernoulli-RIKEN Symposium on Neural Networks and Learning.

Please register through the web-site

http://www.bsis.brain.riken.go.jp/Bernoulli

if you hope to be invited to this Symposium.

Only a limited number of around one hundred people are able to attend
the conference, therefore we may have to decline your requests
according to cicumstances.

Thank you for your understanding and cooperation.

**********************************************
     Bernoulli-RIKEN BSI 2000 Symposium
         on Neural Networks and Learning

Dates and Venues:
  Symposium:    October 25 - 27, 2000
    Ohkouchi Hall, RIKEN (The Institute of Physical and Chemical Research),
　　Japan

  Satellite Workshop:    October 28, 2000
    The Institute of Statistical Mathematics, Japan

Aim:
  In order to celebrate Mathematical Year 2000, The Bernoulli
Society is organizing a number of Symposia in rapidly developing
research areas in which probability theory and statistics
can play important roles.

  Brain science in the wide sense will become more
important in the 21 century.  Information processing in the brain
is so flexible and its learning ability is so strong that it is
indeed a challenge for information science to elucidate its mechanisms.
It is also a big challenge to construct information processing
systems of brain style.

  The present Symposium focuses on learning ability of
real and artificial neural networks and related systems from
theoretical and practical points of view.  Probability theory
and statistics will play fundamental roles in elucidating
these systems, and they in turn fortify stochastic and statistical
methods.

  Theories of neural learning and pattern recognition have a
long history, and lots of new ideas are emerging currently.
They have also practical applicability in the real world problems.
Now is a good time to review all of these new ideas and methods and
to discuss future directions of developments.

  We will invite worldwide top class researchers in these fields
and discuss the state-of-the-art of neural networks and learning
as well as future directions of this important area.  Participants
are by invitation only.  We are expecting 50 -80 participants from
all over the world.

  After the symposium, we will organize a more informal one-day workshop:
"Towards new unification of statistics and neural networks learning".

  The detailed information for time tables and abstracts can be obtained at
	http://www.bsis.brain.riken.go.jp/Bernoulli

  Those who have interests in joining the Symposium and Workshop
may ask invitation through the above web-site after June 15 when we are
ready.  If you have any questions, contact the organizing committee at
	bernoulli2000 at bsis.brain.riken.go.jp

*******************
Sponsors:
   The Bernoulli Society for Mathematical Statistics and Probability
   RIKEN Brain Science Institute
   The Institute of Statistical Mathematics
   Japanese Neural Networks Society

In Cooperation with:
   Japanese Statistical Society

Supported by:
   The Commemorative Association for the Japan World Exposition (1970)
   The Support Center for Advanced Telecommunications research Technology
(SCAT)

Organizing Committee:
  Chair   Shun-ichi Amari,  RIKEN Brain Science Institute, Japan
             Leo Breiman, University of California, Berkeley, USA
             Shinto Eguchi, The Institute of Statistical Mathematics, Japan
             Michael Jordan, University of California, Berkeley, USA
             Noboru Murata, Waseda University, Japan
             Mike Titterington, University of Glasgow, UK
             Vladimir Vapnik,  AT&T, USA

Registration fee 10,000 Japanese yen (nearly 100 US$) (including reception)
is requested at the conference vennue. There is 50% student discount.

*****************
Program:

1.  Graphical Models and Statistical Methods:

  Steffen L. Lauritzen (Aalborg University)
    Graphical models for learning

  Thomas S. Richardson (University of Warwick)
    Ancestral graph Markov models:
    an alternative to models with latent or selection variables

  Lawrence Saul (AT&T Labs)
   Learning the Global Structure of Nonlinear Manifolds 

  Martin Tanner (Northwestern University)
    Inference for and Applications of Hierarchical Mixtures-of-Experts

2.  Combining Learners

  Leo Breiman (University of California, Berkeley)
    Random Forests

  Jerome H. Friedman (Stanford University)
    Gradient boosting and multiple additive regression trees

  Peter Bartlett (Australian National University)
    Large Margin Classifiers and Risk Minimization

  Yoram Singer (The Hebrew University)
    Combining Learners: an Output Coding Perspective

3.  Information Geometry and Statistical Physics

  Shinto Eguchi (The Institute of Statistical Mathematics)
    Information geometry of tubular neighbourhoods
    for a statistical model

  Shun-ichi Amari (RIKEN Brain Science Institute)
    Information geometry of neural networks

  Manfred Opper (Aston University)
     The TAP Mean Field approach for probabilistic models

  Magnus Rattray (University of Manchester)
    Modelling the learning dynamics of latent variable models

4.  VC Dimension and SVM

  Vladimir Vapnik (AT&T Labs)
    Statistical learning theory and support vector machines

  Michael Kearns (AT&T Labs)
    Sparse Sampling Algorithms for Probabilistic Artificial Intelligence

  Gabor Lugosi (Pompeu Fabra University)
    Model selection, error estimation, and concentration

  Bernhard Schoelkopf (Microsoft Research Ltd.)
    SV Algorithms and Applications





********************
Shun-ichi Amari

Vice Director, RIKEN Brain Science Institute
Laboratory for Mathematical Neuroscience
Research Group on Brain-Style Information Systems

tel: +81-(0)48-467-9669; fax: +81-(0)48-467-9687
amari at brain.riken.go.jp
http://www.bsis.brain.riken.go.jp/