Tracking non-stationary dynamical system phase using multi-map and temporal self-organizing architecture

International audienceThis paper presents a multi-map recurrent neural architecture, exhibiting self-organization to deal with the partial observations of the phase of some dynamical system. The architecture captures the dynamics of the system by building up a representation of its phases, coping with ambiguity when distinct phases provide identical observations. The architecture updates the resulted representation to adapt to changes in its dynamics due to self-organization property. Experiments illustrate the dynamics of the architecture when fulfilling this goal

Tracking fast changing non-stationary distributions with a topologically adaptive neural network: Application to video tracking

International audienceIn this paper, an original method named GNG-T, extended from GNG-U algorithm by Fritzke is presented. The method performs continuously vector quantization over a distribution that changes over time. It deals with both sudden changes and continuous ones, and is thus suited for video tracking framework, where continuous tracking is required as well as fast adaptation to incoming and outgoing people. The central mechanism relies on the management of quantization resolution, that cope with stopping condition problems of usual Growing Neural Gas inspired methods. Application to video tracking is briefly presented

Discovering the phase of a dynamical system from a stream of partial observations with a multi-map self-organizing architecture

International audienceThis paper presents a self-organizing architecture made of several maps, implementing a recurrent neural network to cope with partial observations of the phase of some dynamical system. The purpose of self-organization is to set up a distributed representation of the actual phase, although the observations received from the system are ambiguous (i.e. the same observation may correspond to distinct phases). The setting up of such a representation is illustrated by experiments, and then the paper concludes on extensions toward adaptive state representations for partially observable Markovian decision processes

YARBUS : Yet Another Rule Based belief Update System Jérémy Fix Hervé Frezza-Buet

We introduce a new rule based system for belief tracking in dialog systems. Despite the simplicity of the rules being considered, the proposed belief tracker ranks favourably compared to the previous submissions on the second and third Dialog State Tracking challenges. The results of this simple tracker allows to reconsider the performances of previous submissions using more elaborate techniques

A C++ Template-Based Reinforcement Learning Library: Fitting the Code to the Mathematics

International audienceThis paper introduces the rllib as an original C++ template-based library oriented toward value function estimation. Generic programming is promoted here as a way of having a good t between the mathematics of reinforcement learning and their implementation in a library. Main concepts of rllib are presented, as well as a short example

Cellular Computing and Least Squares for partial differential problems parallel solving

The pre-print archived version is not the one that is published, as the editor does not formally allow it.International audienceThis paper shows how partial differential problems can be solved thanks to cellular computing and an adaptation of the Least Squares Finite Elements Method. As cellular computing can be implemented on distributed parallel architectures, this method allows the distribution of a resource demanding differential problem over a computer network

An Empirical Evaluation Framework for Qualifying Dynamic Neural Fields

ISBN : 978-2-9532965-0-1In this paper, the behavior of dynamic neural fields is studied through the lens of performance. As an alternative to the currently available analytical instruments, an empirical evaluation methodology is proposed in order to examine the dynamic quality of a field. This consists of simulating the field through various key scenarios and compare the observed behavior to an optimal expected one. Some desired effects concerning the evolution of an ideal field are inspected, and a performance criterion is defined accordingly. Practically, this approach implements a generic benchmark framework for qualifying neural fields, allowing to inspect the evolution of the model in different key situations. The presented methodology provides a basis for a methodological computational approach towards adjusting the free parameters of the fields in order to satisfy specific desired properties

Can Self-Organization Emerge through Dynamic Neural Fields Computation?

International audienceIn this paper, dynamic neural fields are used to develop key features of a cortically-inspired computational module. Under the perspective of designing computational systems that can exhibit the flexibility and genericity of the cortical substrate, using neural field as the competition layer for self-organizing modules has to be considered. However, despite the fact that they serve as a biologically-inspired model, applying dynamic neural fields to drive self-organization is not straightforward. In order to address that issue, an original method for evaluating neural field equations is proposed, based on statistical measurements of the field behavior in some scenarios. Limitations of classical neural field equations are then quantified, and an original field equation is proposed to overcome these difficulties. The performance of the proposed field model is discussed in comparison with some previously considered models, leading to the promotion of the proposed model as a suitable mean for processing competition in cortex-like computation for cognitive systems

Specialization within cortical models : An application to causality learning

Colloque avec actes et comité de lecture.In this paper we present the principle of learning by specialization within a cortically-inspired framework. Specialization of neurons in the cortex has been observed, and many models are using such "cortical-like" learning mechanisms, adapted for computational efficiency. Adaptations will be discussed, in light of experiments with our cortical model addressing causality learning from perceptive sequences