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CLASS-L  February 2007

CLASS-L February 2007

Subject:

NIPS*2007 - Preliminary Call for Papers

From:

Sumit Basu <[log in to unmask]>

Reply-To:

Classification, clustering, and phylogeny estimation

Date:

Fri, 9 Feb 2007 19:10:46 -0800

Content-Type:

text/plain

Parts/Attachments:

Parts/Attachments

text/plain (128 lines)

PRELIMINARY CALL FOR PAPERS -- NIPS*2007

Deadline for Paper Submissions:  June 8, 2007
 
Submissions are solicited for the Twenty-First Annual meeting of an
interdisciplinary Conference (December 3-6) which brings together
researchers interested in all aspects of neural and statistical processing
and computation. The Conference will include invited talks as well as oral
and poster presentations of refereed papers. It is single track and highly
selective. Preceding the main Conference will be one day of Tutorials
(December 3), and following it will be two days of Workshops at
Whistler/Blackcomb ski resort (December 7-8).
 
Invited Speakers:  To be announced.
 
Tutorial Speakers:  To be announced.
 
Submissions: Papers are solicited in all areas of neural information
processing and statistical learning, including (but not limited to) the
following:

  · Algorithms and Architectures: statistical learning algorithms,
    neural networks, kernel methods, graphical models, Gaussian processes,
    dimensionality reduction and manifold learning, model selection,
    combinatorial optimization.

  · Applications: innovative applications or fielded systems that use
    machine learning, including systems for time series prediction,
    bioinformatics, text/web analysis, multimedia processing, and
    robotics.

  · Brain Imaging: neuroimaging, cognitive neuroscience, EEG
    (electroencephalogram), ERP (event related potentials), MEG
    (magnetoencephalogram), fMRI (functional magnetic resonance imaging),
    brain mapping, brain segmentation, brain computer interfaces.

  · Cognitive Science and Artificial Intelligence: theoretical,
    computational, or experimental studies of perception, psychophysics,
    human or animal learning, memory, reasoning, problem solving, natural
    language processing, and neuropsychology.

  · Control and Reinforcement Learning: decision and control,
    exploration, planning, navigation, Markov decision processes,
    game-playing, multi-agent coordination, computational models of
    classical and operant conditioning.

  · Hardware Technologies: analog and digital VLSI, neuromorphic
    engineering, computational sensors and actuators, microrobotics,
    bioMEMS, neural prostheses, photonics, molecular and quantum
    computing.

  · Learning Theory: generalization, regularization and model selection,
    Bayesian learning, spaces of functions and kernels, statistical
    physics of learning, online learning and competitive analysis,
    hardness of learning and approximations, large deviations and
    asymptotic analysis, information theory.

  · Neuroscience: theoretical and experimental studies of processing and
    transmission of information in biological neurons and networks,
    including spike train generation, synaptic modulation, plasticity and
    adaptation.

  · Speech and Signal Processing: recognition, coding, synthesis,
    denoising, segmentation, source separation, auditory perception,
    psychoacoustics, dynamical systems, recurrent networks, Language
    Models, Dynamic and Temporal models.

  · Visual Processing: biological and machine vision, image processing
    and coding, segmentation, object detection and recognition, motion
    detection and tracking, visual psychophysics, visual scene analysis
    and interpretation.    


Review Criteria: As in the last year, NIPS submissions will be reviewed
double-blind: the reviewers will not know the identities of the authors.
Submissions will be refereed on the basis of technical quality, novelty,
potential impact on the field, and clarity.  There will be an opportunity
after the meeting to revise accepted manuscripts. We particularly encourage
submissions by authors new to NIPS, as well as application papers that
combine concrete results on novel or previously unachievable applications
with analysis of the underlying difficulty from a machine learning
perspective.  

Submission Instructions: NIPS accepts only electronic submissions at

                   http://nips2007.confmaster.net

These submissions must be in PDF format. The Conference web site will accept
electronic submissions until midnight June 8, 2007, Pacific daylight time.
 
Demonstrations: There is a separate Demonstration track at NIPS. Authors
wishing to submit to the Demonstration track should consult the Conference
web site.
 
 
Program Committee:
  Francis Bach (Ecole des Mines de Paris)
  Michael Black (Brown University) 
  Nicolo Cesa-Bianchi (Universitą degli Studi di Milano) 
  Olivier Chapelle (Yahoo! Research) 
  Sanjoy Dasgupta (UC San Diego) 
  Virginia de Sa (UC San Diego) 
  David Fleet (University of Toronto) 
  Isabelle Guyon (ClopiNet) 
  Bert Kappen (University of Nijmegen) 
  Dan Klein (UC Berkeley) 
  Daphne Koller (Stanford)   [Co-Chair]
  Chih-Jen Lin (National Taiwan University) 
  Kevin Murphy (University of British Columbia) 
  William Noble (University of Washington) 
  Stefan Schaal (University of Southern California) 
  Dale Schuurmans (University of Alberta) 
  Odelia Schwartz (Salk Institute and Albert Einstein College of Medicine) 
  Fei Sha (UC Berkeley) 
  Yoram Singer (Google and Hebrew University)   [Co-Chair]
  Mark Steyvers (UC Irvine) 
  Alan Stocker (New York University) 
  Yee Whye Teh (Gatsby Unit, UCL) 
  Nikos Vlassis (Technical University of Crete) 
  Ulrike von Luxburg (MPI for Biological Cybernetics) 
  Chris Williams (University of Edinburgh) 
  Andrew Zisserman (University of Oxford) 
 
Deadline for Paper Submissions: June 8, 2007

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