Search results for "Boltzmann machine"

showing 3 items of 3 documents

Multitasking associative networks.

2012

We introduce a bipartite, diluted and frustrated, network as a sparse restricted Boltzman machine and we show its thermodynamical equivalence to an associative working memory able to retrieve multiple patterns in parallel without falling into spurious states typical of classical neural networks. We focus on systems processing in parallel a finite (up to logarithmic growth in the volume) amount of patterns, mirroring the low-level storage of standard Amit-Gutfreund-Sompolinsky theory. Results obtained trough statistical mechanics, signal-to-noise technique and Monte Carlo simulations are overall in perfect agreement and carry interesting biological insights. Indeed, these associative network…

NeuronsRestricted Boltzmann machineTheoretical computer scienceArtificial neural networkComputer scienceMonte Carlo methodComplex systemGeneral Physics and AstronomyFOS: Physical sciencesStatistical mechanicsDisordered Systems and Neural Networks (cond-mat.dis-nn)Condensed Matter - Disordered Systems and Neural NetworksPhysics and Astronomy (all)Human multitaskingNeural Networks ComputerNerve NetEquivalence (measure theory)Associative propertyPhysical review letters
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Scalability of using Restricted Boltzmann Machines for Combinatorial Optimization

2014

Abstract Estimation of Distribution Algorithms (EDAs) require flexible probability models that can be efficiently learned and sampled. Restricted Boltzmann Machines (RBMs) are generative neural networks with these desired properties. We integrate an RBM into an EDA and evaluate the performance of this system in solving combinatorial optimization problems with a single objective. We assess how the number of fitness evaluations and the CPU time scale with problem size and complexity. The results are compared to the Bayesian Optimization Algorithm (BOA), a state-of-the-art multivariate EDA, and the Dependency Tree Algorithm (DTA), which uses a simpler probability model requiring less computati…

FOS: Computer and information sciencesMathematical optimizationInformation Systems and ManagementOptimization problemGeneral Computer SciencePopulationComputer Science::Neural and Evolutionary Computation0211 other engineering and technologiesBoltzmann machine02 engineering and technologyManagement Science and Operations ResearchIndustrial and Manufacturing EngineeringEvolutionary computation0202 electrical engineering electronic engineering information engineeringNeural and Evolutionary Computing (cs.NE)educationMathematicseducation.field_of_study021103 operations researchArtificial neural networkI.2.6I.2.8Computer Science - Neural and Evolutionary ComputingEstimation of distribution algorithmModeling and SimulationScalabilityCombinatorial optimization020201 artificial intelligence & image processingI.2.6; I.2.8Algorithm
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An implicitly parallel EDA based on restricted boltzmann machines

2014

We present a parallel version of RBM-EDA. RBM-EDA is an Estimation of Distribution Algorithm (EDA) that models dependencies between decision variables using a Restricted Boltzmann Machine (RBM). In contrast to other EDAs, RBM-EDA mainly uses matrix-matrix multiplications for model estimation and sampling. Hence, for implementation, standard libraries for linear algebra can be used. This allows an easy parallelization and leads to a high utilization of parallel architectures. The probabilistic model of the parallel version and the version on a single core are identical. We explore the speedups gained from running RBM-EDA on a Graphics Processing Unit. For problems of bounded difficulty like …

Restricted Boltzmann machineSpeedupEstimation of distribution algorithmArtificial neural networkComputer scienceLinear algebraGraphics processing unitBoltzmann machineParallel computingProceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation
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