[UAI] NIPS*2001 registration

From: Richard Zemel (zemel@cs.toronto.edu)
Date: Thu Oct 11 2001 - 08:30:18 PDT

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    You are invited to attend the 14th annual conference of NIPS*2001,
    Neural Information Processing Systems, at the Hyatt Regency in Vancouver,
    British Columbia, Canada and workshops at the Whistler ski resort near
    Vancouver.

      http://www-2.cs.cmu.edu/Groups/NIPS/

    Tutorials: December 3, 2001
    Conference: December 4-6, 2001
    Workshops: December 6-8, 2001

    The DEADLINE for reduced early registration fees is November 2, 2001.
    Registration can now be made online through a secure credit card link or
    through bank wire transfer, fax, and check:

       https://www.nips.salk.edu/regist.html

    Because the number of submissions this year increased to 650, we were able to
    accept 173 and maintain the same high standards:

       http://www-2.cs.cmu.edu/Groups/NIPS/NIPS2001/nips-program.html

    All registrants this year will receive a CD-ROM of the conference proceedings,
    which will also be available free online. The 2 volume soft-cover format,
    published by the MIT Press, can be purchased at a special conference rate.

    The last month has been a difficult time for everyone. The organizing
    committee for NIPS*2001 has been working hard to ensure that the program and
    facilities for the annual meeting are better than ever. Vancouver is a
    beautiful city with many excellent restaurants within a short walk of
    the conference. The base at Whistler is at 2,200 feet, substantially lower
    than ski resorts in Colorado.

    We hope you will join us in Vancouver for an exciting new NIPS*2001

    Terry Sejnowski

    -----------------------------------------------

    NIPS*2001

    TUTORIALS - December 3, 2001

    Luc Devroye, McGill University - Nonparametric Density Estimation:
        VC to the Rescue
    Daphne Koller, Stanford, and Nir Friedman, Hebrew University -
       Learning Bayesian Networks from Data
    Shawn Lockery, University of Oregon - Why the Worm Turns:
       How to Analyze the Behavior of an Animal and Model Its Neuronal Basis
    Christopher Manning, Stanford University - Probabilistic Linguistics and
    Probabilistic Models of Natural Language Processing
    Bernhard Scholkopf, Biowulf Technologies and Max-Planck Institute for
      Biological Cybernetics - SVM and Kernel Methods
    Sebastian Thrun, Carnegie Mellon University - Probabilistic Robotics

    INVITED SPEAKERS - December 4-6, 2001

    Barbara Finlay, Cornell University - How Brains Evolve, and the
       Consequences for Computation
    Alison Gopnik, UC Berkeley - Babies and Bayes-nets: Causal Inference and
       Theory-formation in Children, Chimps, Scientists and Computers
    Jon M. Kleinberg, Cornell University - Decentralized Network Algorithms:
       Small-world Phenomena and the Dynamics of Information
    Tom Knight, MIT - Computing with Life
    Judea Pearl, UCLA - Causal Inference As an Exercise in Computational Learning
    Shihab Shamma, U. Maryland - Common Principles in Auditory and Visual
    Processing

    WORKSHOPS - December 6-8, 2001

    Activity-Dependent Synaptic Plasticity - Paul Munro
    Artificial Neural Networks in Safety-Related Areas - Johann Schumann
    Brain-Computer Interfaces - Lucas Parra
    Causal Learning and Inference in Humans & Machines - Joshua B. Tenenbaum
    Competition: Unlabeled Data for Supervised Learning - Stefan C. Kremer
    Computational Neuropsychology - Mike Mozer
    Geometric Methods in Learning - Amir H. Assadi
    Information & Statistical Structure in Spike Trains - Jonathon D. Victor
    Kernel-Based Learning - John Shawe-Taylor and Craig Saunders
    Knowledge Representation in Meta-Learning - Ricardo Vilalta
    Machine Learning in Bioinformatics - Colin Campbell, Sayan Mukherjee
    Machine Learning Methods for Text and Images - Jaz Kandola
    Minimum Description Length - Peter Grunwald
    Multi-sensory Perception & Learning - Ladan Shams, John Fisher
    Neuroimaging: Tools, Methods & Modeling - Steve Hanson
    Occam's Razor & Parsimony in Learning - David Stork
    Preference Elicitation - David Poole
    Quantum Neural Computing - Elizabeth Behrman
    Variable & Feature Selection - Isabelle Guyon

    -----------------------------------------------



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