3rd INNS Winter Conference - Trends in Natural and Machine Intelligence
  • 12/10/12 : The conference proceedings are now online at Procedia Computer Science here.
  • 08/09/12 : The presentation instructions are posted now. Also, the tentative technical program is updated here.
  • 02/09/12 : The preliminary technical program is available here. Also, the corresponding author should receive an acknowledgement email from Elsevier this week.
  • 18/07/12 : Notifications have started to be sent out since July 15. The remaining ones should be completed within a couple of days. Thank you for your patience.
  • 31/05/12 : Due to numerous requests, final paper submission deadline is extended to June 15, 2012
  • 11/05/12 : Paper Submission Deadline Extended to May 31, 2012
  • 15/04/12 : Online Submission Opened
  • 25/01/12 : Website Online


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INNS-Winter Conference 2012 (INNS-WC2012)
October 3-5, 2012, Bangkok, Thailand
(text) (pdf)

The flagship conference of the International Neural Network Society (INNS) is the International Joint Conference on Neural Networks (IJCNN) that is jointly sponsored by INNS and IEEE Computational Intelligence Society. IJCNN traditionally features invited plenary talks by world-renowned speakers in the areas of neural network theory and applications, computational neuroscience, robotics, and distributed intelligence. In addition to regular technical sessions with oral and poster presentations, the conference program will include special sessions, competitions, tutorials and workshops on topics of current interest. Typically there are well over six hundred delegates in this annual event.

The board of governors of INNS decided in 2006 to establish a series of symposia or winter conferences devoted to new developments in neural networks. The first of the INNS Symposia Series was held in Auckland, New Zealand back on November 24-25, 2008 – http://www.aut.ac.nz/nnn08/. The theme was “Modeling the brain and the nervous system” and comprised of two symposia: 1) Development and Learning; and 2) Computational Neurogenetic Modelling. The second in the series was the INNS International Education Symposium on Neural Networks (INNS-IESNN) held in Lima, Peru on January 25-27, 2011 – http://eventos.spc.org.pe/inns-iesnn/index.html. The third Symposia Series will cover a much broader context of “Natural and Machine Intelligence”.

INNS Symposium on Nature Inspired Creativity (SoNIC2012) (text) (pdf) (link)

Nature inspired computing techniques have successfully been applied to many different kinds of creative applications, notably, in multimedia content generation and content analysis. However, many research issues are still open in this area. The proposed symposium aims to bring together leading researchers and practitioners in this field who are working on applying natural inspired computing techniques to applications in creative industries. It aims to provide the opportunity to promote, present and discuss on-going research in this area.

With the intensive growth of communication channels in the past decades, our lifestyle is dependent on the Internet and the transaction of multimedia contents has never been at this rate in human civilisation. This brings many emerging research issues on content generation and content analysis. The creative industries need computing facilities that could automate/speed up content creation process. The application of natural computing to this area has gained more and more interest from researchers worldwide.

INNS Symposium on Vision and Image Processing (SoVIP2012) (text) (pdf) (link)

Vision, as in the ability to see, particularly in the form of images, has always played an essential role in everyday human activities. In the past, images were, today they are, and in the future they will continue to be important information carriers. Recent advances in digital imaging and computer hardware technology have led to an explosion in the use of computer vision in a variety of scientific and engineering applications. These applications often arise from the interactions between fundamental scientific researches and development of new and high-standard technologies.

This symposium aims to provide an opportunity for researchers to describe scientific achievements and long-term research challenges, point to new research directions, or provide new insights, or brave perspectives that pave the way to innovation. Subjects of interest are video and image processing, and aspects of related disciplines (such as machine learning, computer graphics, biological vision, mathematics) which illuminate the state of the art in video and image processing.

INNS Symposium on Data Analytics and Competitions (SoDAC2012) (text) (pdf) (link)

Data analytics is fast becoming the norm in various industries and government sectors to provide support for improved decision making. It focuses on the process of inference based on available data and domain knowledge. There is also a push for open data accessibility for the advancement and dissemination of timely scientific content.

SoDAC2012 provides an opportunity for researchers from various disciplines and fields to share and discuss their achievements and challenges, providing novel insights in the interdisciplinary field of data analytics. Subjects of interest range from business analytics to data visualization to knowledge management and discovery. One or more data competitions will also be featured in this event.

INNS Symposium on Autonomous Learning (Autonomous Learning 2012) (link)

Autonomous learning is a very broad term and includes many different kinds of learning. Fundamental to all of them is some kind of a learning algorithm. Whatever the kind of learning, we generally have not been able to deploy the learning systems on a very wide scale, although there certainly are exceptions.

One of the biggest challenges to wider deployment of existing learning systems comes from algorithmic control. Most of the current learning algorithms require parameters to be set individually for almost every problem to be solved. That is, virtually all current approaches to machine learning typically require a human supervisor to design the learning architecture, select the training examples, design the form of the representation of the training examples, choose the learning algorithm, set the learning parameters, decide when to stop learning, and choose the way in which the performance of the learning algorithm is evaluated. This strong dependence on human supervision is greatly retarding the development and ubiquitous deployment autonomous artificial learning systems.”

Autonomous Learning 2012 will cover the topics of autonomous learning, focusing mainly on automation of learning methods that can avoid the kind of dependencies described above.

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