Zriadenie Fakulty informatiky a informaÄnÃ½ch technolÃ³giÃ - FIIT STU

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44 <strong>STU</strong> Faculty of Informatics and Information Technologies−−−−Poláčik, Michal: Adaptation of Recurrent Neural Network Training Algorithms forMultiprocessor Computer Environment. May 2009. Supervisor: M. ČerňanskýPorubčan, Zdenko: Computing Models for Hierarchical Temporal Memory.May 2009. Supervisor: J. ŠtefanovičPrinczkel, Daniel: Adaptation of Neural Networks Using Newton Raphson Method.May 2009. Supervisor: V. KvasničkaSim, Lukáš: Optimalization of Random Forest Algorithm for Prediction Tasks.May 2009. Supervisor: P. Angelovič− Svrček, Matúš: Evolution of Optimal Networks. May 2009. Supervisor: J.Pospíchal−−Šillík, Milan: Multimedial E-learning System about Video Compression. May2009. Supervisor: M. ŠperkaŠimko, Alexander: Symbolic Sequence Processing with Recurrent NeuralNetworks. May 2009. Supervisor: M. Čerňanský− Valaška, Ján: Manipulation with Parts of Different Program's. May 2009.Supervisor: J. Parízková−Varga, Ľubomír: Classification of Visual Data by Neural Networks.May 2009. Supervisor: P. Lacko− Zeman, Jozef: GIS for the Support of Tourism in Towns and Regions. May 2009.Supervisor: M. GalbavýDoctoral (PhD.) ThesesStudent name: Peter LackoDegree program: Artificial IntelligenceThesis title: Emergence of Game Strategy in Multiagent SystemsSupervisor: Vladimír Kvasnička, ProfessorDefended on: June 25, 2009Annotation: In this thesis we focused on subsymbolic approach to machine gameplay problem. We worked on two different methods of learning. Ourfirst goal was to test the ability of common feed-forward neural networksand the mixture of expert topology. We have derived reinforcementlearning algorithm for mixture of expert network topology.This topology is capable to split the problem into smaller parts, whichare easier to be solved by an expert neural network. We have comparedthe quality of strategy emergence between mixture of expertnetworks and feed-forward networks. Our experiments demonstratethat mixture of experts is able to play a game at the same level asfeed-forward networks with equal number of weights. The secondapproach derived in this work is reinforcement learning with usage ofextended Kalman filer. Extended Kalman filter can be used for neuralnetwork training. Its advantage is very high learning rate in terms oftraining cycles. We have proposed usage of extended Kalman filterfor reinforcement learning with TD(0) and Monte Carlo method. Wehave compared the quality of strategy emergence between extended
Annual report 2009 45Kalman filter and TD(l) approach. Our results show that extendedKalman filter is able to create a game strategy after playing a considerablyfewer number of games.Student name: Matej MakulaDegree program: Applied InformaticsThesis title: Benefits and Constrains of Recurrent Neural Networks for ProcessingSymbolic SequencesSupervisor: Ľubica Beňušková, Associate ProfessorDefended on: July 8, 2009Annotation: This thesis studies properties of recurrent neural networks whileprocessing symbolic inputs. We focused mainly on their relation anddescription of their behaviour in the terms of dynamical systems. Wedescribe the dynamics of randomly initialized neural network and itsrelation to Markov prediction models of variable length. In the mainpart of our work we present usability of methods for visualization,clusterization and the state space analysis as an effective tool forthorough study of recurrent networks capabilities on prediction tasks.In experimental part we focus on studying changes occurring duringtraining. We are mostly interested in the change of naïve Markoviandynamics of randomly initialized network during training in relationto various factors such as: input sequence, training algorithm, networkarchitecture, number of hidden units, etc. We focused not onlyon simple recurrent network before and after training, but also on thecomputational capabilities of the new approach called echo state networks.It uses large randomly initialized neural reservoir, which dynamicsis the subject of our interest. We demonstrate benefits andconstraints of this approach based on the results of our experimentsand differences identified after recurrent networks training.Student name: Peter TrebatickýDegree program: Artificial IntelligenceThesis title: Prediction of Dynamical Systems by Recurrent Neural NetworksSupervisor: Jirí Pospíchal, ProfessorDefended on: June 25, 2009Annotation: Recurrent neural networks in general achieve better results in predictionof time series then feedforward networks. Echo state neural networksseem to be one alternative to them. I have shown on the task oftext correction, that they achieve slightly better results compared toalready known method based on Markovov model. The major part ofthis work is focused on alternatives to recurrent neural networkstraining that are based on Kalman filtration modifications. I describein detail the training by filters: Extended Kalman Filter, UnscentedKalman Filter (UKF), nprKF Filter and their joint versions UKFj andnprKFj. Filter UKFj in context of recurrent neural networks wasprobably firstly described in my work. Contribution of this work ispresentation of simpler equations for individual filters, because theyare modified specifically for recurrent neural network training. I
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Zriadenie Fakulty informatiky a informaÄnÃ½ch technolÃ³giÃ - FIIT STU