By offering automated information extraction tools from data, machine learning has revolutionized the way in which humans can cope with electronic data volumes. The ever increasing complexity of the settings continues to pose challenges to the field: often, it is no longer possible to specify a priori a formal learning task; complex parameter choices can severely influence the outcome; and an appropriate encoding of data is not clear at all. More and more often, the human constitutes an important step in the loop to interactively decide about an appropriate learning model, model parameters, and data representation. Because of this fact, intuitive models and model parameters, and human understandable interfaces to the model and data are needed. In this frame, interesting new technologies have been developed such as high quality data visualization tools, sparse interpretable data representation and models, informed priors, active learning, and similar.

This special session aims to foster research in neural learning paradigms which offer an intuitive interface to data or models and thus have the potential as parts of an interactive pipeline. Go to the special session web site more information.

Organisers: Oliver Obst, Mikhail Prokopenko (CSIRO ICT Centre)

Keynote speaker (tentative): Prof. Martin Riedmiller, Albert-Ludwigs University Freiburg, Germany

Learning is one of the most important capabilities of living systems, and processes involved in inference and learning are ingrained in many levels of organisation scale, e.g., from single neurons to organisations or societies. Much of the information processing involved in these processes is consequently distributed, and relies on local interactions among individual entities. On the other hand, many successful approaches from machine learning require some centralised processing, while in living systems no such requirement exists.

In this special session, we are looking for contributions that will address issues that involve information processing, inference, and learning, with a perspective on their application to Alife scenarios. These include for example methods to help characterising relevant information, self-organised encoding of information, decentralised approaches to learning, and inference mechanisms. Possible topics include, but are not limited to,

• information theoretic methods for analysing/describing Alife scenarios
• interactions between genetic evolution, and individual learning
• self-organised information processing
• intrinsically motivated learning
• guided self-organisation

To submit papers to this special session, please use the ACAL 2011 submission system and use Information Processing, Inference and / or Learning as a keyword.

=============================================================

ACAL11 — The Fifth Australian Conference on Artificial Life

6-8 December 2011, Perth, Western Australia

http://robots.newcastle.edu.au/ACAL11/

=============================================================

IMPORTANT DATES

Full Paper Submission: 28 June 2011

Acceptance/Rejection Notification: 15 August 2011

Camera Ready Submission: 10 September 2011

=============================================================

PAPER SUBMISSION AND AUTHOR INFORMATION

The ACAL11 proceedings will be published by Springer-Verlag in the “Lecture Notes in Artificial Intelligence (LNCS/LNAI/LNBI)” Series. Prospective authors are invited to submit their papers in English with no more than 12 pages in Springer LNAI style including results, figures and references. All papers for ACAL11 must be submitted electronically through the easychair conference management system:

http://www.easychair.org/conferences/?conf=acal11

All correctly submitted papers will be peer reviewed by at least three members of the international programme committee.

Paper templates in LNCS/LNAI format are available at Springer-Verlag’s website

http://www.springer.com/computer/lncs?SGWID=0-164-6-793341-0

=============================================================

The symposium will take place at the CSIRO Campus in Sydney (Marsfield), Australia.

The exponentially increasing demand for computing power as well as physical and economic limitations has contributed to a proliferation of distributed and parallel computer architectures. To make better use of current and future high-performance computing, and to fully benefit from these massive amounts of data, we must discover, understand and exploit the available parallelism in machine learning. Simultaneously, we have to model data in an adequate manner while keeping the models as simple as possible, by making use of a sparse representation of the data or sparse modelling of the respective underlying problem.

The invited speakers are:

Samy Bengio (Google Research, CA, USA)
Barbara Hammer (University of Bielefeld, Germany)
Yann LeCun (New York University, NY, USA)
Michael Mahoney (Stanford University, CA, USA)

Call for Papers / Extended Abstracts

Through a combination of invited talks, contributed presentations, discussions and posters, we hope to gain a better understanding of available algorithms and best practices, as well as their inherent limitations.
We are looking for submissions of short papers / extended abstracts (at most 4 pages in NIPS format), in one or more of the following areas:

- Distributed, Multicore and Cluster based Learning Techniques
- Machine Learning on Alternative Hardware (GPUs, Robots, Sensor Networks, Mobile Phones, Cell Processors …)
- Sparsity in Machine Learning and Statistics
- Learning results and techniques on Massive Datasets
- Dimensionality Reduction, Sparse Matrix, Large Scale Kernel Methods
- Fast Online Algorithms for Large Scale Data
- Parallel Computing Tools and Libraries

Selected submissions will be considered for a special issue of a journal or a collected volume on the topic of the symposium. A separate call for papers will then be issued after the event for the special issue/collected volume. Please refer to the web page for further details.

Attendance to DMMD 2011 is free, but limited to approx. 50 participants (first in, best dressed – please register by email).

We can not provide travel support, but for a limited (small) number of interstate/overseas students, we will organise free accommodation. Priority will be given to students with an accepted paper. If you would like to be considered for this, please send an email to apply (deadline 1 November 2010).

Important Dates
Submission deadline: 1 November, 2010
Registration deadline: 31 December, 2010
Symposium: 18-20 January, 2011

Program Committee

Samy Bengio (Google Research, CA, USA)
Joschka Boedecker (Osaka University, Japan)
Stephan Chalup (University of Newcastle, Newcastle)
Tim Cornwell (CSIRO CASS, Sydney)
Ying Guo (CSIRO ICT Centre, Sydney)
Barbara Hammer (University of Bielefeld, Germany)
Yann LeCun (New York University, NY, USA)
Simon Lucey (CSIRO ICT Centre, Sydney)
Michael Mahoney (Stanford University, CA, USA)
N. Michael Mayer (National Chung Cheng University, Taiwan)
Mikhail Prokopenko (CSIRO ICT Centre, Sydney)
Scott Sanner (NICTA & ANU, Canberra)
John A. Taylor (CSIRO CMIS, Canberra)
Rosalind Wang (CSIRO ICT Centre, Sydney)

In this paper, we present an approach to improve the hidden layer of recurrent neural networks, guided by the learning goal of the system, and apply this new method to reservoir computing approaches. Reservoir computing uses, in general, a fixed, randomly initialised hidden layer. A consequence of this is that performance is usually quite good, but not necessarily optimal for the task at hand. There exist self-organised approaches – like intrinsic plasticity – that are able to improve performance of reservoir computing approaches, but usually, they just consider the input to the system, and don’t take the actual task of the system into account.

Our reservoir adaptation optimises the information transfer at each individual unit, dependent on properties of the information transfer between input and (desired) output of the system. Using synthetic data, we show that this reservoir adaptation improves the performance of offline echo state learning and Recursive Least Squares Online Learning.

ICONIP 2010 takes place from the 22nd–25th November 2010 in Sydney, Australia.

icdl, conference, cfp, learning, development, robotics

We invite participation in the conference by researchers in all areas of robotics, automation and mechatronics. At this year’s conference we will be celebrating the 10th Anniversary of ACRA. For more information, please visit the
conference website. Submission of papers deadline is 5 September 2008.

Technorati Tags: cfp, robotics, conference, australia, robots, canberra

Original papers are solicited in all areas of unconventional computation; typical, but not exclusive, topics are: natural computing including quantum, cellular, molecular, neural, and membrane computing as well as evolutionary paradigms; chaos and dynamical systems based computing; proposals for computations going beyond the Turing model.

Submissions are due on April 14th, 2008. The call for papers and the conference poster are available from the conference homepage.

Technorati Tags: unconventional, evolutionary computing, conference, quantum computing, chaos, cfp, neural computing, dynamical systems

Architectures for teams of robots, Ambient Intelligence, Biologically inspired systems, Control issues in multi-robot systems, Distributed decision making/problem solving, Distributed/cooperative perception, Distributed planning, Distributed task execution, Human and robot interaction, Learning and adaptation in teams of robots, Multi-robot applications in exploration, search and rescue, Mobiligence (Emergence of Intelligence through Mobility), Modular robotics, Network robotics, Performance metrics for robot teams, Reconfigurable robots, Robot societies, Self-organizing robotic systems, Sensor networks, Swarm robotics, Task allocation.

The conference takes place in Tsukuba, Ibaraki, Japan, Nov. 17-19, 2008. Full paper submission is June 30, 2008. For details, check out the web page.

Technorati Tags: CFP, conference, robotics, distributed systems, autonomous robots

Several artificial life themes have been proposed as live research topics around which conference sessions may organise. See here for details.

The conference will be held in Winchester, paper submission is 29 February, conference date is 5-8 August, 2008.