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High Performance Associative Neural Networks: Overview and Library High Performance Associative Neural Networks: Overview and Library Presented at AI06, Quebec city, Canada, June 7-9, 2006 Oleksiy K. Dekhtyarenko 1 and Dmitry O. Gorodnichy 2 1 - Institute of Mathematical Machines and Systems, Dept. of Neurotechnologies, 42 Glushkov Ave., Kiev, 03187, Ukraine. olexii@mail.ru 2 - Institute for Information Technology, National Research Council of Canada, M-50 Montreal Rd, Ottawa, Ontario, K1A 0R6, Canada. dmitry.gorodnichy@nrc.gc.ca http://synapse.vit.iit.nrc.ca

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2 Associative Neural Network Model Features: Distributed storage of information fault tolerance Parallel way of operation efficient hardware implementation Non-iterative learning rules fast, deterministic training Confirms to three main principles of neural processing: 1.Non-linear processing 2.Massively distributed collective decision making 3.Synaptic plasticity 1.to accumulate learning data in time by adjusting synapses 2.to associate receptor to effector (using thus computed synaptic values) The Associative Neural Network (AsNN) is a dynamical nonlinear system capable of processing information via the evolution of its state in high dimensional state-space.

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3 Examples of Practical Applications Face recognition from video * Electronic Nose ** * D. Gorodnichy – Associative Neural Networks as Means for Low-Resolution Video-Based Recognition, IJCNN05 ** A. Reznik; Y. Shirshov; B. Snopok; D. Nowicki; O. Dekhtyarenko & I. Kruglenko – Associative Memories for Chemical Sensing, ICONIP'02

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4 Associative Properties Convergence Process Network evolves according to the state update rule: – set of memorized patterns We want the network to be retrieve data by associative similarity (to restore noisy or incomplete input data):

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5 Sparse Associative Neural Network Advantages over Fully-Connected Model: Less memory needed for s/w simulation Quicker convergence during s/w simulation Fewer and/or more suitable connections for h/w implementation Greater biological plausibility Output of neuron i can affect neuron j (w ij 0) if and only if: Architecture, or Connectivity Template: Connection Density:

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6 Network Architectures Random Architecture 1D Cellular Architecture Small-World Architecture 1 – the worst 5 – the best Associative Performance Memory Consumption Hardware Friendly Regular (cellular) 155 Small-World 254 Scale-Free 253 Random 352 Adaptive 452 Fully-Connected 511

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7 Compare to … Fully connected net with n=24x24 neurons obtained by tracking and memorizing faces (of 24x24 pixel resolution) from real-life video sequences [Gorodnichy 05] Notice visible inherent synaptic structure ! This synaptic interdependency is utilized by Sparse architectures.

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8 Some Learning Algorithms Projective Hebbian (Perceptron LR) Delta Rule Pseudo-Inverse – selection operator, where 1.Performance Evaluation Criteria 1.Performance Evaluation Criteria Error correction capability (Associativity strength) Capacity Training complexity Memory requirements Execution time: a) in Learning and b) in Recognition

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9 Comparative Performance Analysis Networks with Fixed Architectures Associative performance and training complexity as a function of number of stored patterns Cellular 1D network with dimension 256 and connection radius 12, randomly generated data vectors

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10 Comparative Performance Analysis Influence of Architecture Sparse network with dimension 200, randomly generated data vectors, various ways of architecture selection Associative performance as a function of connection density PI WS – PseudoInverse Weight Select, architecture targeting maximum informational capacity per synapse PI Random – Randomly set sparse architecture with PseudoInverse learning rule PI Cell – Cellular architecture with PseudoInverse learning rule PI WS Reverse – architecture constructed using the opposite criterion of PI WS

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11 Associative Neural Network Library Publicly available at http://synapse.vit.iit.nrc.ca/memory/pinn/library.html Effective C++ implementation of full and sparse associative networks Includes noniterative Pseudo-Inverse LR with possibility of addition/removal of selected vectors to/from memory Different learning rules: Projective, Hebbian, Delta Rule, Pseudo-Inverse Different architectures: fully-connected, cellular (1D and 2D), random, small-world, adaptive Desaturation Technique: allows to increase memory capacity up to 100% Different update rules: synchro. vs. asynchro. Detection of cycles Different testing functions: absolute and normalized radius of attraction, capacity Associative Classifiers: Convergence-based, Modular

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12 Associative Neural Network Library Hierarchy of Main Classes

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