oliver.obst.eu

We are organising a special session “Information Processing, Inference and Learning” at the Fifth Australian Conference on Artificial Life (ACAL11).

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.

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Our new paper Phase transitions in least-effort communications has been published in J. Stat. Mech. In this work, we critically examine a model that attempts to explain the emergence of power laws (e.g., Zipf’s law) in human language. The model is based on the principle of least effort in communications—specifically, the overall effort is balanced between the speaker effort and listener effort, with some trade-off. It has been shown that an information-theoretic interpretation of this principle is sufficiently rich to explain the emergence of Zipf’s law in the vicinity of the transition between referentially useless systems (one signal for all referable objects) and indexical reference systems (one signal per object). The phase transition is defined in the space of communication accuracy (information content) expressed in terms of the trade-off parameter. Our study (more…)

DMMD 2011 Symposium: Distributed machine learning and sparse representation with massive data sets
Web page: http://research.ict.csiro.au/conferences/machine-learning/

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

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Our new paper, Improving Recurrent Neural Network Performance using Transfer Entropy, has been accepted at the 17th International Conference on Neural Information Processing (ICONIP) in 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.

The Third International Workshop on Guided Self-Organisation (GSO-2010) will be held at Indiana University in Bloomington, Indiana, USA, 4-6 September 2010.

The workshop is comprised of a group of researchers with diverse yet related interests, overlapping in the area of self-organizing systems and methods for characterizing those systems in ways that may ultimately allow them to be guided toward prespecified goals. Information theory and graph theory are core to many of these methods; quantifying complexity and its sources a common theme.

If interested in participating, send an extended abstract to the email addresses on the workshop web site.  Selected works from the workshop will likely be published in a special journal issue (as has been the case in the past).  More information on the GSO-2010 web site.

guided self-organisation, workshop, artificial life, ai

Our new paper describes a mathematical model for generic neural microcircuits, with potential engineering applications, as well as implications to understand how networks in biology are shaped to be optimally adapted to requirements of their environment.

Reservoir computing (RC) is a recent paradigm in the field of recurrent neural networks. Networks in RC have a sparsely and randomly connected fixed hidden layer, and only output connections are trained. RC networks have recently received increased attention as a mathematical model for generic neural microcircuits to investigate and explain computations in neocortical columns. Applied to specific tasks, their fixed random connectivity, however, leads to significant variation in performance. Few problem-specific optimization procedures are known, which would be important for engineering applications, but also in order to understand how networks in biology are shaped to be optimally adapted to requirements of their environment. We study a general network initialization method using permutation matrices and derive a new unsupervised learning rule based on intrinsic plasticity (IP). The IP-based learning uses only local learning, and its aim is to improve network performance in a self-organized way. Using three different benchmarks, we show that networks with permutation matrices for the reservoir connectivity have much more persistent memory than the other methods but are also able to perform highly nonlinear mappings. We also show that IP-based on sigmoid transfer functions is limited concerning the output distributions that can be achieved.

neurophysiology, optimisation, physiological models, recurrent neural nets, unsupervised learning, reservoir computing, echo state networks

The principle of least effort in communications has been shown, by Ferrer i Cancho and Solé, to explain emergence of power laws (e.g., Zipf’s law) in human languages. In our new paper, Origins of Scaling in Genetic Code (O. Obst, D. Polani, M. Prokopenko), published on ECAL 2009, we  apply the principle and the information-theoretic model of Ferrer i Cancho and Solé to genetic coding. The application of the principle is achieved via equating the ambiguity of signals used by “speakers” with codon usage, on the one hand, and the effort of “hearers” with needs of amino acid translation mechanics, on the other hand. The re-interpreted model captures the case of the typical (vertical) gene transfer, and confirms that Zipf’s law can be found in the transition between referentially useless systems (i.e., ambiguous genetic coding) and indexical reference systems (i.e., zero-redundancy genetic coding). As with linguistic symbols, arranging genetic codes according to Zipf’s law is observed to be the optimal solution for maximising the referential power under the effort constraints. Thus, the model identifies the origins of scaling in genetic coding — via a trade-off between codon usage and needs of amino acid translation. Furthermore, the paper extends Ferrer i Cancho ­ Solé model to multiple inputs, reaching out toward the case of horizontal gene transfer (HGT) where multiple contributors may share the same genetic coding. Importantly, the extended model also leads to a sharp transition between referentially useless systems (ambiguous HGT) and indexical reference systems (zero-redundancy HGT). Zipf’s law is also observed to be the optimal solution in the HGT case.

genetic code, evolution, language, zipf’s law, horizontal gene transfer

ICDL is a multidisciplinary conference pertaining to all subjects related to
the development and learning processes of natural and artificial systems,
including perceptual, cognitive, behavioral, emotional and all other mental
capabilities that are exhibited by humans, higher animals, and robots. Its
visionary goal is to understand autonomous development in humans and higher
animals in biological, functional, and computational terms, and to enable such
development in artificial systems. ICDL strives to bring together researchers
in neuroscience, psychology, artificial intelligence, robotics and other
related areas to encourage understanding and cross-fertilization of latest
ideas. ICDL2009 is held in Shanghai, June 5-7, 2009.
For a list of topics of see the CfP at http://www.icdl09.org/.

icdl, conference, cfp, learning, development, robotics

Special issue of the Elsevier Journal of Algorithms in Cognition, Informatics and Logic.

Submissions connected to the following non-exhaustive list of topics are particularly encouraged:

• new learning paradigms of RNNs such as unsupervised learning or reservoire learning
• biologically plausible methods
• integration of RNNs and symbolic reasoning
• universal approaches for general data structures such as sets or graphs
• methods which address the generalization ability of RNNs
• challenging applications which have the potential to be benchmark problems
• visionary papers concerning the future of RNNs

Deadline for submissions is 18th of July, 2008.

Technorati Tags: neural networks, rnn, journal, cfp, special issue, recurrent neural networks

At IPSN 2008, I’m going to present our work “Using Echo State Networks for Anomaly Detection in Underground Coal Mines”. In this work, we investigate the problem of identifying anomalies in monitoring critical gas concentrations using a sensor network in an underground coal mine. In this domain, one of the main problems is a provision of mine specific anomaly detection, with cyclical (moving) instead of flatline (static) alarm threshold levels. An additional practical difficulty in modelling a specific mine is the lack of fully labelled data of normal and abnormal situations. We present an approach addressing these difficulties based on echo state networks learning mine specific anomalies when only normal data is available. Echo state networks utilize incremental updates driven by new sensor readings, thus enabling a detection of anomalies at any time during the sensor network operation. We evaluate this approach against a benchmark — Bayes Network based anomaly detection, and observe that the quality of the overall predictions is comparable to the benchmark. However, the echo state networks maintain the same level of predictive accuracy for data from multiple sources. Therefore, the ability of echo state networks to model dynamical systems make this approach more suitable for anomaly detection and predictions in sensor networks. Check out the details here.

Technorati Tags: Sensor networks, Echo State Networks, Neural Networks, Anomaly detection, IPSN, 2008, Paper, Computer Science