[Comp-neuro] Deadline Extension: MLINI-2012:  2nd  Workshop on Machine Learning and Inference in Neuroimaging at NIPS-2012

Irina Rish rish at us.ibm.com
Sun Sep 23 06:19:52 CEST 2012



 Call for Papers



       MLINI-2012:  2nd  Workshop on Machine Learning and Inference in
      Neuroimaging at NIPS-2012

       https://sites.google.com/site/nipsmlini2012/

       December 7-8, 2012, Lake Tahoe, Nevada, United States

       Submission deadline (extended): October 8, 2012

       Workshop Overview:
       ------------------------

       MLINI is a two day workshop on the topic of machine learning
       approaches in neuroscience and neuroimaging. We believe that both
       machine learning and neuroimaging can learn from each other as the
       two communities overlap and enter an intense exchange of ideas and
       research questions. Methodological developments in machine learning
       spurn novel paradigms in neuroimaging, while neuroscience motivates
       methodological advances in computational analysis. In this context
       many controversies and open questions exist. The goal of the
       workshop is to pinpoint these issues, sketch future directions, and
       tackle open questions in the light of novel methodology.

       The first workshop of this series at NIPS 2011 built upon earlier
       events in 2006 and 2008. Last year's workshop included many invited
       speakers, and was centered around two panel discussions, during
       which 2 questions were discussed: the interpretability of machine
       learning findings, and the shift of paradigms in the neuroscience
       community. The discussion was inspiring, and made clear, that there
       is a tremendous amount the two communities can learn from each other
       benefiting from communication across the disciplines.  The aim of
       the 2nd MLINI workshop is to continue exploring important  issues on
       the intersection of ML and neuroimaging and further promote cross-
       fertilization between   these communities. Besides interpretation,
       and the shift of paradigms, many open questions remain. Among them:

       How suitable are MVPA and inference methods for brain mapping?
       How can we use these approaches for a flexible and useful
       representation of neuroimaging data?
       What is the role of decoding vs. embedded or separate feature
      selection?
       How can we assess the specificity and sensitivity?
       What can we accomplish with generative vs. discriminative modelling?

       Can and should the Machine Learning community provide a standard
       repertoire of methods for the Neuroimaging community to use (e.g. in
       choosing a classifier)?

       Workshop Format:
       --------------------------

       In this two-day workshop we will explore perspectives and novel
       methodology at the interface of Machine Learning, Inference,
       Neuroimaging and Neuroscience. We aim to bring researchers from
       machine learning and neuroscience community together, in order to
       discuss open questions, identify the core points for a number of the
       controversial issues, and eventually propose approaches to solving
      those issues.

       The workshop will be structured around the following main topics:
       - machine learning and pattern analysis methodology in neuroimaging
       - causal inference and interpretability in neuroimaging
       - evaluation of machine learning  methods  in light of clinical
      applications
       - linking machine learning methodology with neuroscience or
       neuroimaging questions
       Each session will be opened by 2-3 invited talks, and an in depth
       discussion. This will be followed by original contributions.
       Original contributions will also be presented and discussed during a
       poster session. The workshop will end with a panel discussion,
       during which we will address specific questions, and invited
       speakers will open each segment with a brief presentation of their
      opinion.

       This workshop proposal is part of the PASCAL2 Thematic Programme on
       Cognitive Inference and Neuroimaging  (
      http://mlin.kyb.tuebingen.mpg.de/ ).

       Paper Submission:
       --------------------------
       We seek for submission of original (previously unpublished) research
       papers. The length of the submitted papers should not exceed 8 pages
      in
       Springer format,  excluding the references (LaTeX2e style  files are
       available on the workshop page).
       Submission of previously published work is possible as well, but the
       authors are required to mention this explicitly. Previously
       published work can be presented at the workshop, but will not be
       included into the workshop proceedings (which are considered peer-
       reviewed publications of novel contributions).  Moreover, the
       authors are welcome to present their novel work but choose to opt
       out of the workshop proceedings in case they have alternative
       publication plans.

       Important dates:
       --------------------------
       -  October 1, 2012 - paper submission
       -  October 15, 2012  -  notification of acceptance/rejection
       -  December 7-8, 2012 -  Workshop in Lake Tahoe, Nevada US,
      following the
       NIPS conference

       Invited Speakers:
       --------------------------
       Jack Gallant (UC Berkeley)
       Bertrand Thirion (INRIA, Neurospin)
       Jean-Baptiste Poline (Neurospin)
       Mert Sabuncu (MGH, Harvard Medical School)

               (more to be confirmed)


       Organizing Committee:
       --------------------------
       Guillermo Cecchi (IBM T.J. Watson Research Center)
       Kai-min Kevin Chang (Language Technologies Institute, Carnegie
      Mellon University)
       Moritz Grosse-Wentrup (Max Planck Institute for Intelligent Systems,
      Tübingen, Germany)
       Georg Langs (Medical University of Vienna, CSAIL, MIT)
       Bjoern Menze (ETH Zuerich, CSAIL, MIT)
       Brian Murphy (Machine Learning Department, Carngie Mellon
      University)
       Irina Rish (IBM T.J. Watson Research Center)
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