THIRD INTERNATIONAL WORKSHOP ON COGNITIVE INFORMATION PROCESSING
May 28 to 30, 2012 Parador de Baiona
CALL FOR PAPERS
Following the success of the previous editions of the Workshop on
Cognitive Information Processing (CIP), we are pleased to announce the
third one in this series*.
This workshop aims at bringing together researchers from the machine
learning, pattern recognition, statistical signal processing, social
sciences, communications and radar communities in an effort to promote
The machine learning group of the department of computer science of K.U.Leuven has several open positions for PhD students and post-docs, amongst others in the context of the projects
- ERC Starting Grant "MiGraNT: Mining Graphs and Networks, a Theory-based approach", https://dtai.cs.kuleuven.be/research/projects/ERC2009MiGraNT
- KULeuven OT project "Probabilistic structured models: learning from large-scale hybrid domains", http://dtai.cs.kuleuven.be/research/projects/OTPSM11
Call for participation
ACTIVITY RECOGNITION CHALLENGE
Human activity recognition can be used to devise assistants that
provide proactive support by exploiting the knowledge of the user’s
context, determined from sensors located on-body. Notwithstanding the
large amount of research endeavours on this field, the comparison of
different approaches is often not possible due to the lack of common
benchmarking tools and datasets that allow for replicable and fair
testing procedures across several research groups.
Applications are invited for two fully funded PhD students to work in
the School of Informatics on the following topics:
* knowledge transfer to automate learning visual models
* learning visual object categories from consumer and advertisement videos
* leveraging the structure of natural sentences to aid visual learning
Applicants must have:
* Master degree (preferably in Computer Science or Mathematics)
* Excellent programming skills; the projects involve programming
in Matlab and C++
The UK-EPSRC funded project "Learning to Recognise Dynamic Visual Content from Broadcast Footage" is a collaboration between the University of Surrey (Prof Richard Bowden), the University of Oxford (Prof Andrew Zisserman) and the University of Leeds (Dr Mark Everingham) with research staff appointed at each institution.
We are running the PASCAL Visual Object Classes Recognition Challenge again this year. As in 2010 there are 20 object classes for the main competitions. Participants can recognize any or all of the classes, and there are classification, detection and pixel-wise segmentation competitions. This year the action classification taster competition has a new "other" category, and there is also a taster competition on person layout (detecting head, hands, feet). There is also an associated large scale visual recognition taster competition organized by www.image-net.org.
Spectral methods for learning tree-structured graphical models.
Supervision : François Denis and Liva Ralaivola, Université
Deadline : 08/20/2011
A PhD studentship is available as part of the french ANR funded project
LAMPADA on "Learning Algorithms, Models an sPArse representations for
structured DAta" being jointly undertaken by Inria Lille Nord Europe (Marc
Tommasi), the Laboratoire d’Informatique Fondamentale de Marseille
(François Denis), the Laboratoire Hubert Curien de Saint-Etienne (Marc
9-th Summer School on Dat Mining, Maastricht, The Netherlands
An intensive 4-day introduction to methods and applications
Department of Knowledge Engineering, Maastricht University,
Maastricht, The Netherlands
August 29 - September 1, 2011
Most business organizations collect terabytes of data about business
processes and resources. Usually these data provide just "facts and
figures", not knowledge that can be used to understand and eventually
Probabilistic inference in graphical models is a key task in many applications, from machine vision to computational biology. Since the
problem is generally computationally intractable many approximations have been suggested over the years.
The goal of this challenge is to evaluate inference algorithms on difficult large scale problems.
Some of challenge highlights are:
- Algorithms for MAP, marginals, and partition function approximation will be evaluated.
- Solvers will be evaluated on different time scales (20 seconds, 20 minutes, 1 hour).
Supervision: Dr Mark Everingham, University of Leeds
Deadline: Open until filled
A PhD studentship is available as part of an EPSRC funded project on "Learning to Recognise Dynamic Visual Content from Broadcast Footage" being jointly undertaken by the University of Leeds (Mark Everingham), the University of Oxford (Andrew Zisserman), and the University of Surrey (Richard Bowden).