PhD student / postdoc positions in PLT+AD

Seeking PhD students and postdocs interested in an elegant combination
of functional programming and big-iron style numeric computing.

Functional Programming and Automatic Differentiation
PhD Studentships
Postdoctoral Positions

We are adding exact first-class derivative calculation operators
(Automatic Differentiation or AD) to the lambda calculus, and
embodying the combination into a production-quality optimising
compiler. Our research prototype compiler generates object code
competitive with the fastest current systems, which are based on
FORTRAN. We are seeking PhD students and postdocs with interest and
experience in relevant areas: programming language theory, numeric
computing/numeric linear algebra, or differential geometry; and a
burning drive to help lift big iron numeric computing out of the 1960s
and into a newer higher order. Specific sub-projects include:
compiler and numeric programming environment construction; writing,
simplifying, and generalising numeric algorithms through the use of AD
operators; and associated type/lambda calculus/PLT/real computation
issues.

The project headquarters will be in the Hamilton Institute, NUI
Maynooth, Ireland, http://www.hamilton.ie/.

Applications to:
“Barak A. Pearlmutter”

French Spring School in Machine Learning

We invite applications to attend the 1st French Spring School in Machine
Learning (EPAT 2010) which will be held at Cap Hornu, in the beautiful Baie
de Somme, from may 2 to may 7 2010. Detailed information is available at

http://www.hds.utc.fr/epat2010/

Targeted Audience:

The school is meant for PhD students, young researchers in machine
learning and researchers from other research areas, such as signal
processing or bioinformatics.
Limited grants are available for PASCAL researchers.
Note that the lectures will be given in French.

Application deadline: April 2, 2010

Aims:

Our objective is to gradually present a few important themes, from basics to
recent advances. The selected themes are:

– Machine Learning: a Historical Perspective
– Optimization Techniques for Non-differentiable Problems in ML
– Kernel Methods
– Data Analysis
– Bioinformatics

Confirmed Lecturers:

Stéphane Chrétien, Université de Franche Comté
Antoine Cornuéjols, AgroParisTech
Marco Cuturi, Princeton University
Mohamed Nadif , Université Paris Descartes
Stéphane Robin, Institut National de la Recherche Agronomique (INRA)
Jean-Philippe Vert, Mines ParisTech & Institut Curie, INSERM

Program Committee:

Stéphane Canu, LITIS, INSA Rouen
Antoine Cornuéjols, LRI, AgroParisTech
François Denis, LIF, Université de Provence
Rémi Gilleron, INRIA, Lille
Yves Grandvalet, Heudiasyc, Université de Technologie de Compiègne
Rémi Munos, Mostrare INRIA, Université de Lille
Liva Ralaivola, LIF, Université de Provence
Marc Sebban, Laboratoire Hubert Curien, Université Jean Monnet
Jean-Philippe Vert, Mines ParisTech & Institut Curie, INSERM

Organisers:

Heudiasyc, CNRS and University of Compiègne joint research unit, France
CNRS, Nord-Pas de Calais & Picardie delegation.

Supported by PASCAL, Heudiasyc, CNRS, UT Compiègne, Picardie, INRIA, GdR BiM, SFdS

Special Issue Call for Papers: Model and Optimization for Machine Learning -*DEADLINE EXTENDED*

Details of the call can be found at the following link:

http://www.pascal-network.org/cfp/specialissuenov09.pdf

The paper submission deadline has been extended to 28 February 2010

Second call for papers: MLSP 2010, the Twentieth IEEE International Workshop on Machine Learning for Signal Processing

Second Call for Papers

for the

Twentieth IEEE International Workshop on
Machine Learning for Signal Processing
(MLSP 2010)

August 29 – September 1, 2010, Kittila, Finland

Website: http://mlsp2010.conwiz.dk

IMPORTANT DATES:

Submission of full papers: April 1, 2010
Notification of acceptance: May 28, 2010
Camera-ready paper
and author registration: June 18, 2010
Advance registration before: June 23, 2010

The 2010 IEEE International Workshop on MACHINE LEARNING FOR SIGNAL
PROCESSING (MLSP 2010) will be held in Kittila, Finland, in
August-September 2010. MLSP 2010 is the twentieth workshop in the
series of workshops sponsored by IEEE Signal Processing Society. It
will present the most recent and exciting contributions in machine
learning for signal processing through keynote talks as well as
special and regular single-track sessions.

INVITED SPEAKERS:

– Prof. Zoubin Ghahramani, University of Cambridge
– Prof. Tom Mitchell, Carnegie Mellon University
– Dr. Henry Tirri, Head of Nokia Research Center

ORGANIZATION:

General chair: Erkki Oja
Program chairs: Samuel Kaski, David Miller
Special session chairs: Samy Bengio, Mikko Kurimo
Publicity chairs: Marc Van Hulle, Jaakko Peltonen
Web and publication chairs: Antti Honkela, Jan Larsen
Data competition chairs: Vince Calhoun, Kenneth Hild, Mikko Kurimo
Local arrangements: Tapani Raiko (chair), Francesco Corona,
Ali Faisal, Mari-Sanna Paukkeri

VENUE:

MLSP 2010 will be held in the Levi Summit conference and exhibition
centre in Kittila, Finland. Levi is one of the largest resorts in
Finnish Lapland, north of the Arctic Circle. In the summer, Levi
offers many sports activities as well as lots of wild northern nature.
The conference centre is located high on the hillside of the Levi
fell, accessible by gondola from the main village.

CONFERENCE TOPICS:

Machine learning in signal processing is concerned with tasks such as
detection, estimation, prediction, classification, and optimization,
with a wide range of applications. The following is a non-exhaustive
list of topics for MLSP 2010:

– Bayesian learning and signal processing
– Cognitive information processing
– Graphical and kernel methods
– Information-theoretic learning
– Learning theory and algorithms, including bounds on performance
– Supervised learning, including signal detection, pattern
recognition and classification
– Unsupervised learning, reinforcement learning
– Source separation and component analysis
– Data fusion and integration
– Feature extraction, information visualization
– Sparse and structured representations
– Neural network learning
– Time-series analysis
– Adaptive filtering
– Data mining, information retrieval
– Sequential learning and sequential decision methods
– Hardware implementation of machine learning in signal processing
– Applications of machine learning: Bioinformatics, Biomedical and
neural signal processing, Neuroinformatics, Speech and audio
processing, Image and video processing, Computer vision,
Sensor networks, Robot control, Communications, Cognitive radio,
Multimodal interfaces and context modeling, Intelligent multimedia
and web processing

SPECIAL SESSION:

A special session “Towards multimodal proactive interfaces using
large-scale machine learning” is being organized. For more
information see http://mlsp2010.conwiz.dk .

DATA COMPETITION:

In conjunction with the workshop, a data and signal analysis
competition “Mind Reading” is being organized. Winners will present
their works and receive their award during the Workshop. For more
information see http://mlsp2010.conwiz.dk .

PAPER SUBMISSION PROCEDURE:

Authors are invited to submit a double column paper of up to six pages
using the electronic submission procedure described at
http://mlsp2010.conwiz.dk .

Accepted papers will be published by IEEE Press; electronic
proceedings will be distributed at the workshop and included in
IEEE Xplore.

JOURNAL SPECIAL ISSUE:

Authors of selected papers will be invited to submit extended versions
to a special issue of an international journal.

SPONSORS: MLSP 2010 is supported by IEEE, by the IEEE Signal
Processing Society, by the PASCAL2 Network of Excellence, and by
the Federation of Finnish Learned Societies. The data competition is sponsored by Nokia and the PASCAL2 Challenge Program.

========= See http://mlsp2010.conwiz.dk for more details! =========

Workshop on Active Learning and Experimental Design (collocated with AISTATS)

Workshop on Active Learning and Experimental Design
—————————————————————————-
Collocated with AISTATS 2010
May 16, 2010
Chia Laguna resort
http://clopinet.com/isabelle/Projects/AISTATS2010/

Abstract submission deadline: March 8, 2010

Guest speakers:
– Donald Rubin, Department of Statistics, Harward University (co-inventor of the EM algorithm, expert in experimental design and causal inference)
– Burr Settles, Machine Learning Department, Carnegie Mellon University (author of an authoritative tutorial on active learning)
– David Jensen, Computer Science Department, Universtity of Massachusetts (expert in quasi-experimental design)

The results of the Active Learning challenge http://clopinet.com/al will be discussed at the workshop and plans will be made for an upcoming challenge on experimental design.

ICDL (deadline EXTENDED): 9th International Conference on Development and Learning

Submission deadline EXTENDED to March 6

9th International Conference on Development and Learning (ICDL)
University of Michigan, Ann Arbor, MI, USA
http://www.icdl-2010.org
August 18-21, 2010

ICDL is the premiere venue for interdisciplinary research that blends the boundaries between robotics, artificial intelligence, machine learning, developmental psychology, neuroscience, and philosophy. The scope of development and learning covered by this conference includes perceptual, cognitive, motor, behavioral, emotional and other related capabilities that are exhibited by humans, higher animals, artificial systems and robots.

While most other conferences focus on either mechanisms or organisms, ICDL focuses on both! The papers presented at the conference are split approximately 50-50 between the “natural intelligence side,” such as neuroscience and psychology, and the “artificial intelligence side,” such as machine intelligence and robotics. This diversity is mirrored in the composition of the organizing committee and the ICDL governing board. Please join us in 2010 when we celebrate our 10-th anniversary.

Topics of interest include, but are not limited to:

* General principles of development
* Cognitive and perceptual development
* Developmental learning: schedules and architectures
* New methodologies to study natural and artificial intelligence.
* Statistical learning in humans and machines
* Embodied cognition
* Play and exploration in animals, infants and robots
* Interactive learning
* Cultural learning
* Social and emotional development
* Theory of mind
* Language acquisition
* Skill acquisition
* Intrinsic motivation
* Dynamic systems
* Attention mechanisms and their role in development
* Philosophical issues of development and learning
* Differences between learning and development
* Interactions of learning and development with evolution
* Grounding of knowledge and representations
* Studies and models of developmental disorders, e.g., autism
* Using robots to study development and learning
* Human-Robot interaction
* Visual, auditory, and tactile systems and their development
* Motor systems and their development
* Biological and biologically inspired developmental architectures
* Neural plasticity during development.

ICDL 2010 will accept two types of submissions:

1) Full six-page paper submissions. Accepted papers will be included
in the conference proceedings and will be selected for either an oral
presentation or a featured poster presentation. Featured posters
will have a 1 minute “teaser” presentation as part of the main
conference session and will be showcased in the poster sessions.

2) Two-page poster abstract submissions. To encourage late-breaking
results or for work that is not sufficiently mature for a full paper,
ICDL will accept 2-page abstracts. These submissions will NOT be
included in the conference proceedings. Accepted abstracts will be
presented during the evening poster sessions.

Important dates:
** Mar 06, 2010 Full 6-page paper submissions due ** Changed from Feb 20
May 20, 2010 Notification of accept/reject for papers
May 27, 2010 2-page poster abstracts due
June 10, 2010 Notification of accept/reject for abstracts
June 20, 2010 Camera-Ready Copy due
July 20, 2010 Early Registration Deadline
Aug. 18-21, 2010 Conference

General Chairs:
* Benjamin Kuipers, University of Michigan
* Thomas Shultz, McGill University

Program Chairs:
* Alexander Stoytchev, Iowa State University
* Chen Yu, Indiana University, Bloomington

Publicity chairs:
* Ian Fasel, University of Arizona, USA (for North America)
* Jochen Triesch, Frankfurt Institute for Advanced Studies, Germany (for Europe)
* Jun Tani, RIKEN, Japan (for Asia).

Sponsored by:
* IEEE Computational Intelligence Society
* Cognitive Science Society

For more information please check the conference web site:
http://www.icdl-2010.org/

Sony and Oxford Brookes Research Fellow, computer vision for computer games

The Sony EyeToy was launched onto the video games market five years ago. Since then, it has found its way into over 8 million homes worldwide. Using a small video camera plugged into a PlayStation, it tracks the player’s motion, allowing them to play games by moving their bodies rather than using a controller.

Sony Computer Entertainment Europe and Oxford Brookes University have established a Knowledge Transfer Partnership (KTP), aimed at transferring recent results in the academic community into gaming experience for camera based games. We are seeking a Research Associate to lead this 27 month project.

The main responsibility of the post holder will be to research and develop machine learning and computer vision methods and algorithms including:

· Hand tracking

· Interactive computer games

· Image Segmentation

with the aim of developing new camera based games based on this research for the EyeToy.

You should have:

· a good first degree in maths, engineering, physics or computer science

· a PhD or equivalent research experience in computer vision and machine learning

· strong academic and commercial knowledge of computer vision

· ability to research and develop new computer vision algorithms

· good C++

· strong mathematics

· a passion for computer games

You will be self motivated, organised and good at communicating. You should be able to create novel computer vision algorithms and turn these into production quality systems. You should be able to take direction and also contribute to the overall strategy and goals of the group.

Informal inquiries to:

philiptorr(at)brookes.ac.uk,

Professor Phil Torr,
Department of Computing,
Oxford Brookes University,
Wheatley,
Oxford
OX33 1HX.
http://cms.brookes.ac.uk/staff/PhilipTorr/

The Synthetic Visual Reasoning Test Challenge

The first dead-line has been moved to August 31, 2010.

This competition is part of the PASCAL2 challenge program
http://pascallin2.ecs.soton.ac.uk/Challenges/

INTRODUCTION

We are pleased to announce a new challenge for machine learning and
computer vision: The Synthetic Visual Reasoning Test (SVRT). One
motivation is to expose some limitations of current methods for
pattern recognition, and thereby to argue for making a larger
investment in other paradigms and strategies, emphasizing the pivotal
role of relationships among parts, complex hidden states and a rich
dependency structure.

This test consists of a series of 23 hand-designed, image-based,
binary classification problems. The images are binary and with
resolution 128×128. For each problem we have implemented a generator
in C++, which allows one to produce as many i.i.d samples as desired.
A pdf document containing examples of images is available at

http://www.idiap.ch/~fleuret/svrt/svrt.pdf

The Bayes error rate of each problem is virtually zero, and nearly all
of them can be perfectly solved by humans after seeing fewer than ten
examples from each class. Nonetheless, some of them are probably as
difficult as various “real” problems featured in previous challenges
and widely known data-sets. In particular, solving these synthetic
visual tasks with high accuracy requires “reasoning” about
relationships among shapes and their poses.

Human experiments were conducted in the laboratory of Prof. Steven
Yantis, a cognitive psychologist at Johns Hopkins University; those
results will appear in a future publication. A number of people were
asked to solve the problems and the number of samples required to
master each concept was recorded.

SVRT challenge participants who follow the rules described below and
whose results are noteworthy for either their originality or sheer
performance will be invited to co-author a comprehensive, and
hopefully visible, article summarizing the performance of their
methods, including a discussion of the performance of humans (and
possibly monkeys) on the same tasks.

CHALLENGE

The generators for a randomly-selected subset of 13 problems are made
available to participants. Using these 13 problems as “case studies,”
the challenge is to develop or adapt a learning algorithm which inputs
a training set and outputs a classifier for labeling a binary image.

An important performance metric is the number of training examples
required to obtain any given accuracy. Algorithms should be designed
to be trained on sets of varying sizes.

Participants have until August 31, 2010, for development, and are
required to make public the results achieved on the 13 problems as
well as the source code required to reproduce these results and to
test the algorithm on other problems.

The source code and test error rates must be sent to the challenge
organizers Francois Fleuret (francois.fleuret(at)idiap.ch) and Donald
Geman (geman(at)jhu.edu) before midnight EST, August 31, 2010.

The test error rates must be provided in a single text file, with one
line per problem and number of training examples. At minimum, results
are to be provided for exactly 10, 100 and 1000 training examples per
class per problem. Participants may choose to also send their results
for higher powers of ten. On each line there should be the problem
number, followed by the number of training samples, followed by ten
test error rates estimated on ten different runs, with 10,000 test
samples per class. Numbers should be separated by commas.

On September 1, 2010, we will publish the ten remaining problems
(i.e., make the generators available). Participants will measure the
performance of their algorithms *with no additional change* on this
new set of problems and send the performance by mail to the challenge
organizers before midnight EST, September 31, 2010. At that point, we
may use the participants’ code to verify the reported performance.

* DOWNLOAD

The source code of the generators can be downloaded from

http://www.idiap.ch/~fleuret/svrt/

A pdf document containing ten samples of each class of each problem,
together with the error rate of a baseline classifier trained with
Boosting, is available at

http://www.idiap.ch/~fleuret/svrt/svrt.pdf

* CONTACT

François Fleuret, Idiap Research Institute
francois.fleuret(at)idiap.ch
http://www.idiap.ch/~fleuret/

Donald Geman, Johns Hopkins University
geman(at)jhu.edu
http://www.cis.jhu.edu/people/faculty/geman/

Postdoc Position in Computer Vision and Machine Learning

Details: http://pub.ist.ac.at/~chl/Postdoc-CV.html
Contact: Christoph Lampert

A postdoc position in Computer Vision and Machine Learning is available
immediately at the Institute of Science and Technology Austria (IST
Austria) in the group of Christoph Lampert.

Applicants should hold a PhD and have experience in computer vision,
machine learning and optimization methods. Prior knowledge of modern
machine learning techniques (in particular kernel methods, structured
output learning, and/or probabilistic approaches) will be an advantage,
a strong analytical background is a must. We are looking a for highly
motivated and creative individual who enjoys working in an excellent
research environment including adequate funding for equipment and
conference travel. The successful candidate will have no mandatory
teaching or administrative duties, but he or she should be motivated to
take an active role in the further development of the newly established
research group. Good communication skills and fluency in English are
required. German language skills are optional.

Conditions of employment: The post-doctoral position is provided for up
to two years with very competitive salary. The starting dates are
flexible. There is no fixed deadline, applications will be considered
until the position is filled, as announced on
http://pub.ist.ac.at/~chl/Postdoc-CV.html

Application procedure: Formal applications should include CV, a
statement of research experience and interests, list of publications,
academic transcripts, as well as the contact details of three
references. Please send applications as single PDF document to Prof.
Christoph Lampert .

About the institute: IST Austria (www.ist.ac.at) is a new institute that
opened its campus near Vienna in 2009. It is dedicated to basic research
in the natural sciences and related disciplines
(www.ist.ac.at/research). Established by the Austria Government, IST
Austria has substantial funding, allowing for over 500 employees and
graduate students by 2016. The language of the Institute is English. IST
Austria is committed to equality and diversity. In particular female
applicants are encouraged to apply.

Postdoctoral position at IDIAP (CH)

Postdoctoral position in multimodal processing
for interaction with robots at Idiap Research Institute (CH)

The IDIAP Research Institute (www.idiap.ch), associated with EPFL
(Swiss Federal Institute of Technology, Lausanne) seeks a qualified
candidate for one postdoctoral research position in computer vision,
audio processing, and machine learning for multimodal interaction in
robots.
The positions is available immediately.

The research will be conducted in the context of the Humavips project
funded by the European Commission
(http://perception.inrialpes.fr/rubrique.php3?id_rubrique=7).
The position offers the opportunity to collaborate with prominent European
research teams in robotics, vision, and multimodal interaction. The
overall goal of the project is to endow humanoid robots with
audio-visual sensing and interaction capabilities for navigation in
complex environments, person localization, and social
interaction. Specific research areas involve the design of perceptual
algorithms to recognize human nonverbal behavior from audio-visual
sensors; new approaches to identify people interactions and
relationships; and principled methods to exploit physical and social
context for effective human-robot interaction.

The postdoctoral researcher should have a strong background in machine
learning, computer vision, audio processing, or robotics. Experience
in one or several of the following areas is required: human tracking,
event recognition and discovery, and human-robot or human-computer
multimodal interfaces. The applicant should also have strong
programming skills. The position is for one year with possibilities
of renewal based on performance. Salaries are competitive.

Idiap is located in Martigny in Valais, a scenic region in the south
of Switzerland surrounded by the highest mountains of Europe, which
offers multiple recreational activities, including hiking, climbing,
and skiing, as well as varied cultural activities, all within close
proximity to Lausanne and Geneva. Idiap is an equal opportunity
employer and offers a young, multicultural environment where English
is the main working language.

For further details and application please contact:

Jean-Marc Odobez (odobez(at)idiap.ch, tel : +41 (0)27 721 77 26)
Daniel Gatica-Perez (gatica(at)idiap.ch, tel : +41 (0)27 721 77 33)