ICANN 2011: Call for Workshops and Tutorials

CALL FOR WORKSHOP AND TUTORIAL PROPOSALS

We invite researchers interested in chairing a workshop or a tutorial
at ICANN 2011, The Twentieth Anniversary International Conference on
Artificial Neural Networks, to submit proposals for workshops and
tutorials.

ICANN 2011 brings together the two main sources of inspiration for
research on computational modeling of adaptive and learning systems:
– statistical and mathematical principles, and
– human brain and cognition.

VENUE:

ICANN 2011 will take place at the Aalto University School of Science
and Technology (former Helsinki University of Technology) and Dipoli
Conference Center. They are located in Espoo, Finland in the Helsinki
capital area.

DATES:

Submission of workshop and tutorial proposals: December 3rd, 2010
Notification of workshop acceptance: December 17th, 2010
Notification of tutorial acceptance: January 7th, 2011
ICANN conference: June 14-17th, 2011

WORKSHOP AND TUTORIAL ORGANIZATION:

The topics of workshops and tutorials should be related to the ICANN
research areas, including brain inspired computing, machine learning,
and applications.

Workshops are meetings for talks and informal discussion of important
problems and challenges. One full day will be allocated for workshops.
Workshop organizers are responsible for the content of a workshop and
coordinating participation.

Each tutorial should last from two to four hours.

SUBMISSION INSTRUCTIONS:

Proposals are to be submitted by email to the address below by
December 3rd, 2010. The proposals should include:
– a title
– a short description of the problem to be addressed
– a motivation for the proposed topic
– names of proposed speakers
– names, email addresses and phone numbers of all the organizers
(please indicate the primary contact)

Tutorial proposals should in addition include a resume of the
prospective lecturer with a list of publications to establish
scholarship in the field.

CONTACT INFORMATION:

Proposals or questions should be sent to
icann2011.workshops(at)cis.hut.fi (for workshops)
or
icann2011.tutorials(at)cis.hut.fi (for tutorials)

NIPS 2010 workshop: Monte Carlo Methods for Bayesian Inference in Modern Day Applications

*** Deadline: October 31, for 1-page abstracts.

—————————————————————————
NIPS 2010 workshop
Monte Carlo Methods for Bayesian Inference in Modern Day Applications
http://montecarlo.wikidot.com/
http://nips.cc/

December 10, 2010
Whistler, Canada. Westin Resort and Spa and Hilton Resort and Spa
Sponsored by the PASCAL2 EU Network of Excellence
—————————————————————————

We invite submissions on Monte Carlo methods and their practical
application. Particularly welcome are “tricks of the trade” and “war
stories” that might not make it into conventional publications.
Submissions are solicited both from researchers developing new
methodology and from practitioners using established techniques.

Send poster abstracts of up to one page to
montecarlo-nips2010(at)cs.toronto.edu
by Oct 31, 2010. Use the NIPS style file with no anonymity. We will
notify acceptances by Nov 4, before the NIPS early registration
deadline.

We intend to invite key contributions from the workshop to submit full
papers to a JMLR W&CP issue to appear in the new year.

We also invite contributions to the wiki, including suggested readings
and discussion topics:
http://montecarlo.wikidot.com/

The organizers:
Ryan Prescott Adams, http://www.cs.toronto.edu/~rpa/
Mark Girolami, http://www.dcs.gla.ac.uk/inference/
Iain Murray, http://homepages.inf.ed.ac.uk/imurray2/

Confirmed invited speakers:
Derek Bingham
Julien Cornebise
Arnaud Doucet
Andrew McCallum
Yee-Whye Teh
Max Welling

Workshop description:

Monte Carlo methods have been the dominant form of approximate inference for
Bayesian statistics over the last couple of decades. Monte Carlo methods are
interesting as a technical topic of research in themselves, as well as enjoying
widespread practical use. In a diverse number of application areas Monte Carlo
methods have enabled Bayesian inference over classes of statistical models which
previously would have been infeasible. Despite this broad and sustained
attention, it is often still far from clear how best to set up a Monte Carlo
method for a given problem, how to diagnose if it is working well, and how to
improve under-performing methods. The impact of these issues is even more
pronounced with new emerging applications. This workshop is aimed equally at
practitioners and core Monte Carlo researchers. For practitioners we hope to
identify what properties of applications are important for selecting, running
and checking a Monte Carlo algorithm. Monte Carlo methods are applied to a broad
variety of problems. The workshop aims to identify and explore what properties
of these disparate areas are important to think about when applying Monte Carlo
methods.

We look forward to seeing you in Whistler this December!

Call for contributions – New Problems and Methods in Computational Biology [NIPS 2010 MLCB workshop]

Call for contributions

New Problems and Methods in Computational Biology

http://www.mlcb.org

A workshop at the Twenty-Third Annual Conference on
Neural Information Processing Systems (NIPS 2010)
Whistler, BC, Canada, December 10 or 11, 2010.

Deadline for submission of extended abstracts: Oct 25, 2010,

WORKSHOP DESCRIPTION

The field of computational biology has seen dramatic growth over
the past few years, in terms of newly available data, new
scientific questions and new challenges for learning and
inference. In particular, biological data is often relationally
structured and highly diverse, and thus requires combining multiple
weak evidence from heterogeneous sources. These sources include
sequenced genomes of a variety of organisms, gene expression data
from multiple technologies, protein sequence and 3D structural
data, protein interaction data, gene ontology and pathway
databases, genetic variation data (such as SNPs), high-content
phenotypic screening data, and an enormous
amount of text data in the biological and medical literature. These
new types of scientific and clinical problems require novel
supervised and unsupervised learning approaches that can use these
growing resources.

The workshop will host presentations of emerging problems and
machine learning techniques in computational biology. We encourage
contributions describing either progress on new bioinformatics
problems or work on established problems using methods that are
substantially different from standard approaches. Kernel methods,
graphical models, semi-supervised approaches, feature selection
and other techniques applied to relevant bioinformatics problems
would all be appropriate for the workshop.

SUBMISSION INSTRUCTIONS

Researchers interested in contributing should upload an extended
abstract of 4 pages in PDF format to the MLCB submission web site
http://www.easychair.org/conferences/?conf=mlcb2010

by Oct 25, 2010, 11:59pm (Samoa time).

No special style is required. Authors may use the NIPS style file, but
are also free to use other styles as long as they use standard font
size (11 pt) and margins (1 in).

All submissions will be anonymously peer reviewed and will be
evaluated on the basis of their technical content. A strong
submission to the workshop typically presents a new learning method
that yields new biological insights, or applies an existing learning
method to a new biological problem. However, submissions that improve
upon existing methods for solving previously studied problems will
also be considered. Examples of research presented in previous years
can be found online at http://www.mlcb.org/nipscompbio/previous/.

Please note that accepted abstracts will be posted online at
www.mlcb.org. Authors may submit two versions of their abstract, a
longer version for review and a shorter version for posting to the web
page. In addition, we intent to make presentations be video taped and
published online as part of the videolectures.net website supported by
Pascal.

The workshop allows submissions of papers that are under review or
have been recently published in a conference or a journal. This is
done to encourage presentation of mature research projects that are
interesting to the community. The authors should clearly state any
overlapping published work at time of submission. Authors of
accepted abstracts will be invited to submit full length versions
of their contributions for publication in a special issue of BMC
Bioinformatics.

ORGANIZERS

Gunnar Rätsch,
Friedrich Miescher Laboratory of the Max Planck Society

Tomer Hertz,
Fred Hutchinson Cancer Research Center

Yanjun Qi,
Machine Learning Department, NEC Research

Jean-Philippe Vert,
Mines ParisTech, Institut Curie

PROGRAM COMMITTEE

Mathieu Blanchette, McGill University
Gal Chechik, Google Research
Florence d’Alche-Buc, Université d’Evry-Val d’Essonne, Genopole,
Eleazar Eskin, UC Los Angeles,
Brendan Frey (University of Toronto)
Alexander Hartemink (Duke University)
David Heckerman, Microsoft Research ,
Michael I. Jordan, UC Berkeley ,
Christina Leslie, Memorial Sloan-Kettering Cancer Research Center,
Michal Linial, The Hebrew University of Jerusalem ,
Quaid Morris, University of Toronto,
Klaus-Robert Müller, Fraunhofer FIRST ,
William Stafford Noble, Department of Genome Sciences, University of
Washington
Dana Pe’er, Columbia University ,
Uwe Ohler, Duke University ,
Alexander Schliep, Rutgers University,
Koji Tsuda, Computational Biology Research Center
Alexander Zien, LIFE Biosystems

PhD Studentships in Probabilistic Machine Learning

A number of PhD studentships are available in the newly established

Probabilistic Machine Learning group, headed by Matthias Seeger
School of Computer and Communication Sciences
Ecole Polytechnique Federale de Lausanne (EPFL)
http://upseeger.epfl.ch/

The group focusses on the development and analysis of scalable Bayesian
inference and graphical modelling technology, with challenging applications to
Bayesian experimental design (adaptive compressive sensing, active learning),
medical imaging (magnetic resonance imaging), low-level computer vision, signal
and image processing, and modelling of neural recordings, as well as
learning-theoretical characterizations of such probabilistic setups.

It is part of Europe’s highest ranked computer and communication sciences
faculty at EPFL, one of the leading technical universities worldwide, a unique
surrounding for study and research, where collaborations with top scientists
in computer vision, medical and scientific imaging, signal processing, and
information theory can be forged. EPFL is beautifully located at the shores
of Lake Geneva, offering views of the highest peaks of the Alps, and the
Lausanne area is known for its numerous cultural festivals.

Openings are available for exceptional students with excellent mathematical
background and very high motivation for research in probabilistic machine
learning, approximate Bayesian inference, and applications thereof. Admission
to the doctoral program is internationally competitive.

Application to the doctoral program EDIC is centralized. Please refer to

http://phd.epfl.ch/page-19698-en.html

for any details concerning the application process.

*** Do NOT reply to this mail or send me your documents, but submit them
*** through the site. Applications which are not centrally submitted,
*** cannot be considered.
*** Please indicate in your submission that you would want to work with
*** me (listing other faculty as well is perfectly fine), as this will
*** flag your application for me. You may want to indicate to me that you
*** have applied, but please do not expect a direct answer.

The deadline for applications is

January 15, 2011

Candidates who happen to attend the forthcoming Neural Information Processing
Systems conference (Vancouver, December 2010), should make themselves known
to me there.

Relevant links:

– EDIC doctoral school:
http://phd.epfl.ch/page-19698-en.html
– Research in the Probabilistic Machine Learning group:
http://upseeger.epfl.ch/
– Computer and Communication Sciences, EPFL:
http://ic.epfl.ch/

University of Trento: Post-doc and research professor positions available

The CLIC laboratory of the Center for Mind/Brain Sciences (CIMeC) of
the University of Trento announces the availability of:

TWO 2-YEAR POST-DOC POSITIONS

– one post-doc position in the computational neuroscience of language

– one post-doc position in the construction of multimodal semantic
spaces (partially funded by a Google Research Award)

Read below for details.

The Center for Mind/Brain Sciences (CIMeC) is also offering a number
of attractive

FIXED-TERM RESEARCH PROFESSOR POSITIONS

for advanced post-doctoral researchers with an interest in any area of
the cognitive (neuro)sciences, including language and computational
models.

* Research environment *

The Language Interaction and Computation lab (clic.cimec.unitn.it) is
a unit of the University of Trento’s Center for Mind/Brain Sciences
(www.cimec.unitn.it) or CIMEC: an interdisciplinary center for the
research in brain and cognition including neuroscientists,
psychologists, (computational) linguists, computational
neuroscientists, and physicists. CLIC consists of researchers from the
Departments of Computer Science and Cognitive Science carrying out
research on a range of topics, including concept acquisition and
information extraction from very large multimodal corpora, combining
brain data and data from corpora to study cognition, and methods of
theoretical linguistics.

* Post-doc position in computational neuroscience of language *

A 2-year post-doctoral position in the computational neuroscience of
language with a focus on the organization of conceptual knowledge in
the brain will soon become available at CIMeC/CLIC.

The successful candidate will work as part of a larger project whose
objective is to combine empirical data of different types (corpus
co-occurrence patterns, elicitation experiments, neuroimaging data) to
arrive at a better understanding of the organization of conceptual
knowledge in the mind and brain. Your task will be to continue
on-going work which uses machine learning methods to extract
conceptual representations from recordings of neural activity (EEG,
MEG and fMRI).

The candidate should have technical knowledge of computational
linguistics and machine learning, and familiarity with theories of
ontologies and the lexicon. Programming skills are a must, and
experience with neuroimaging techniques, experimental design
(elicitation/behavioural), machine learning and signal processing
would be a plus.

* Post-doc position in multimodal semantic spaces *

A 2-year post-doctoral position on multimodal semantic spaces is
available at CIMeC/CLIC. The scholarship is partially sponsored by a
Google Research Award, and the project will be carried out as a
collaboration between CLIC members and the Zurich Google Research
team.

The automated measurement of semantic similarity between
words/concepts through semantic space models such as Latent Semantic
Analysis or Topic Models has been a success story in text mining
(Turney and Pantel 2010). Today, through the Web, we have access to
huge amounts of documents that contain both text and images. While the
use of text to improve image labeling and retrieval is an active and
growing area of research (e.g., Wang et al. 2009), in this project we
go the other way around, and develop novel techniques to extract
multimodal semantic spaces from texts and images, in order to improve
the measurement of semantic similarity among words. A recent trend in
computer vision represents images as vectors that record the
occurrence, in the analyzed image, of a discrete vocabulary of “visual
words” (Yang et al. 2007). This development paves the way to the
integration of visual word cooccurrence features into the text-based
vectors of current semantic space models. The topic is expected to
have a strong impact both on the applied front, as a breakthrough in
the acquisition of large semantic repositories, and from a theoretical
point of view, leading to “embodied” models of computational learning
that are more directly comparable to what human learners do (Barsalou,
2008).

The successful candidate will have a strong computational background,
including familiarity with machine learning and/or statistical
methods, and have conducted research in either natural language
processing or computer vision. An interest in exploring the
connections between artificial and natural intelligence and cognition
is also desirable.

* How to apply *

For additional information please send an expression of interest (with
CV) to:

– computational neuroscience: Brian Murphy (brian.murphy(at)unitn.it)
– multimodal semantic spaces: Marco Baroni (marco.baroni(at)unitn.it)
– research professorships: Massimo Poesio (massimo.poesio(at)unitn.it)

Research Fellow and PhD position at Oxford Brookes University

2 year research fellowship start around 1st Feb 2011

And 3.5 year Phd position.

To work with Philip Torr at Oxford Brookes vision research group
http://cms.brookes.ac.uk/staff/PhilipTorr/

contact philiptorr(at)brookes.ac.uk

To engage in state of the art research in computer vision. In particular the work will be an EPSRC grant “Scene Understanding using New Global Energy Models”.

This proposal concerns scene understanding from video. Computer vision algorithms for individual tasks such as object recognition, detection and segmentation has now reached some level of maturity. The next challenge is to integrate all these algorithms and address the problem of scene understanding. The problem of scene understanding involves explaining the whole image by recognizing all the objects of interest within an image and their spatial extent or shape in 3D.

The first application to drive the research will be the problem of automated understanding of cities from video using computer vision, inspired by the availability of massive new data sets such as that of Google’s Street View http://maps.google.com/help/maps/streetview/, Yotta http://www.yotta.tv/index.php (who have agreed to supply Oxford Brookes with data) and Microsoft’s Photosynth http://labs.live.com/photosynth/. The scenario is as follows: a van drives around the roads of the UK, in the van are GPS equipment and multiple calibrated cameras, synchronized to capture and store an image every two metres; giving a massive data set. The task is to recognize objects of interest in the video, from road signs and other street furniture, to particular buildings, to allow them to be located exactly on maps of the environment. A second scenario would be to perform scene understanding for indoor scenes such as home or office, with video taken from a normal camera and Z-cam.

NIPS 2010 Workshop on Modeling Human Communication Dynamics

———————————————————
NIPS Workshop on Modeling Human Communication Dynamics Friday, December 10th,
2010 Whistler, British Columbia, Canada http://projects.ict.usc.edu/hcd2010/
———————————————————

Description

Face-to-face communication is a highly interactive process in which the participants mutually
exchange and interpret verbal and nonverbal messages. Both the interpersonal dynamics and
the dynamic interactions among an individual’s perceptual, cognitive, and motor processes are
swift and complex. How people accomplish these feats of coordination is a question of great
scientific interest. Models of human communication dynamics also have much potential
practical value, for applications including the understanding of communications problems
such as autism and the creation of socially intelligent robots able to recognize, predict, and
analyze verbal and nonverbal behaviors in real-time interaction with humans.

Modeling human communicative dynamics brings exciting new problems and challenges to
the NIPS community. The first goal of this workshop is to raise awareness in the machine
learning community of these problems, including some applications needs, the special
properties of these input streams, and the modeling challenges. The second goal is to
exchange information about methods, techniques, and algorithms suitable for modeling
human communication dynamics. After the workshop, depending on interest, we may
arrange to publish full-paper versions of selected submissions, possibly as a volume in the
JMLR Workshop and Conference papers series.

Topics

We invite submissions of short high-quality papers describing research on Human
Communication Dynamics and related topics. Suitable themes include, but are not limited to:

* Modeling methods robust to semi-synchronized streams (gestural,
lexical, prosodic, etc.)
* Learning methods robust to the highly variable response lags seen in
human interaction
* Coupled models for the explicit simultaneous modeling of more than
one participant
* Ways to combine symbolic (lexical) and non-symbolic information
* Learning of models that are valuable for both behavior recognition
and behavior synthesis
* Algorithms robust to training data with incomplete or noisy labels
* Feature engineering
* Online learning and adaptation
* Models of moment-by-moment human interaction that can also work for
longer time scales
* Failures and problems observed when applying existing methods
* Insights from experimental or other studies of human communication
* Concrete applications

Invited speakers

* Janet Bavelas (University of Victoria)
* Marian Stewart Bartlett (University of California, San Diego)
* Jeff Bilmes (University of Washington)
* Dan Bohus (Microsoft Research)
* Justine Cassell (Carnegie Mellon University)
* Noah D. Goodman (Stanford University)

Submission guidelines

Submissions should be written as extended abstracts, no longer than
4 pages in the NIPS latex style. NIPS style files and formatting instructions can be found at
http://nips.cc/PaperInformation/StyleFiles
(we will not enforce the double blind rule). Work that was recently published or presented
elsewhere is allowed, provided that the extended abstract mentions this explicitly; work
earlier presented at non-ML venues is especially encouraged. Please send your submission by
email to hcd2010@ict.usc.edu by October 18th, 2010 at 11:59pm PDT.

Important dates

Submission deadline (extended): October 18th, 2010, 11:59pm PDT Notification of
acceptance: November 7th, 2010
Workshop: December 10th, 2010

Organizers

Louis-Philippe Morency, University of Southern California Daniel Gatica-Perez, Idiap
Research Institute Nigel Ward, University of Texas, El Paso

Sponsored by the PASCAL 2 European Network of Excellence on Pattern Analysis,
Statistical Modeling, and Computational Learning

Call for papers – NIPS Workshop – Low-rank Matrix Approximation for Large-scale Learning

===============================================
CALL FOR PAPERS

Low-rank Matrix Approximation for Large-scale Learning
NIPS 2010 Workshop, Whistler, Canada
December 11, 2010

http://www.eecs.berkeley.edu/~ameet/low-rank-nips10/

Submission Deadline: October 31, 2010
===============================================

OVERVIEW

Today’s data-driven society is full of large-scale datasets. In the context of machine learning,
these datasets are often represented by large matrices representing either a set of real-valued
features for each point or pairwise similarities between points. Hence, modern learning
problems in computer vision, natural language processing, computational biology, and other
areas often face the daunting task of storing and operating on matrices with thousands to
millions of entries. An attractive solution to this problem involves working with low-rank
approximations of the original matrix. Low-rank approximation is at the core of widely used
algorithms such as Principle Component Analysis, Multidimensional Scaling, Latent Semantic
Indexing, and manifold learning. Furthermore, low-rank matrices appear in a wide variety of
applications including lossy data compression, collaborative filtering, image processing, text
analysis, matrix completion and metric learning.

The NIPS workshop on “Low-rank Matrix Approximation for Large-scale Learning” aims to
survey recent work on matrix approximation with an emphasis on usefulness for practical
large-scale machine learning problems. We aim to provide a forum for researchers to discuss
several important questions associated with low-rank approximation techniques.
The workshop will begin with an introductory talk and will include invited talks by
Emmanuel Candes (Stanford), Ken Clarkson (IBM
Almaden) and Petros Drineas (RPI). There will also be several contributed paper talks as well
as poster session for contributed papers.

We encourage submissions exploring the impact of low-rank methods for large-scale machine
learning in the form of new algorithms, theoretical advances and/or empirical results. We also
welcome work on related topics that motivate additional interesting scenarios for use of low-
rank approximations for learning tasks. Some specific areas of interest include randomized
low-rank approximation techniques, the effect of data heterogeneity on randomization,
performance of various low-rank methods for large-scale tasks and the tradeoff between
numerical precision and time/space efficiency in the context of machine learning
performance, e.g., classification or clustering accuracy.

SUBMISSION GUIDELINES

Submissions should be written as extended abstracts, no longer than 4 pages in the NIPS
latex style. Style files and formatting instructions can be found at
http://nips.cc/PaperInformation/StyleFiles.
Submisssions must be in PDF format. Authors names and affiliations should be included, as
the review process will not be double blind.
The extended abstract may be accompanied by an unlimited appendix and other
supplementary material, with the understanding that anything beyond 4 pages may be
ignored by the program committee.

Please send your PDF submission by email to submit.lowrank@gmail.com by October 31.
Notifications will be given on or before November 15.
Topics that were recently published or presented elsewhere are allowed, provided that the
extended abstract mentions this explicitly.

ORGANIZERS

Michael Mahoney (Stanford), Mehryar Mohri (NYU, Google Research), Ameet Talwalkar
(Berkeley)

PROGRAM COMMITTEE

Alexandre d’Aspremont (Princeton), Christos Boutsidis (Rensselear Polytechnic Institute),
Kamilika Das (NASA Ames Research Center), Maryam Fazel (Washington), Michael I.
Jordan (Berkeley), Sanjiv Kumar (Google Research), James Kwok (Hong Kong University of
Science and Technology), Gunnar Martinsson (Colorado at Boulder)

Reconceiving Machine Learning – Postdoctoral Research Position in Machine Learning at Australian National University

A postdoctoral research position is available at the Australian National University for a
project Reconceiving Machine Learning.

Project: http://users.cecs.anu.edu.au/~williams/rml.html

Job: http://jobs.anu.edu.au/PositionDetail.aspx?p=1541

Closing: 24 October 2010

Postdoctoral Research Position in Machine Learning at Australian National University

There is a 2.5 year postdoctoral research fellow position available immediately at the
Australian National University. The position is associated with the project Structures and
Protocols for inference and the successful application would work with Prof Bob Williamson
and Dr Mark Reid.

For further details and instructions to apply see

http://jobs.anu.edu.au/PositionDetail.aspx?p=1520