Suchergebnisse - Numerical algorithms for specific classes of architectures

A Universal Approximation Result for Difference of Log-Sum-Exp Neural Networks von Calafiore, Giuseppe C., Gaubert, Stephane, Possieri, Corrado

“… By using a logarithmic transform, this class of network maps to a family of subtraction-free ratios of generalized posynomials (GPOS …”
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Random-Effect Models for Degradation Analysis Based on Nonlinear Tweedie Exponential-Dispersion Processes von Chen, Zhen, Xia, Tangbin, Li, Yaping, Pan, Ershun

“… If such a specific degradation model is wrongly assumed, then poor analysis results of reliability assessment would be obtained …”
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Architectures and accuracy of artificial neural network for disease classification from omics data von Yu, Hui, Samuels, David C., Zhao, Ying-yong, Guo, Yan

“… Background Deep learning has made tremendous successes in numerous artificial intelligence applications and is unsurprisingly penetrating into various …”
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Alternative Distributed Algorithms for Network Utility Maximization: Framework and Applications von Palomar, D.P., Mung Chiang

“… structures as a way to obtain different distributed algorithms, each with a different tradeoff among convergence speed …”
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A Parallel Adaptive Coupling Algorithm for Systems of Differential Equations von Garbey, M., Tromeur-Dervout, D.

“… In this paper we address the challenge of metacomputing with two distant parallel computers linked by a slow network and running the numerical approximation of two sets of coupled PDEs …”
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Universal Approximation Abilities of a Modular Differentiable Neural Network von Wang, Jian, Wu, Shujun, Zhang, Huaqing, Yuan, Bin, Dai, Caili, Pal, Nikhil R.

“… Moreover, an adequate level of interpretability of the networks is missing as well. In this work, we propose a class of new network architecture, built with reusable neural modules (functional blocks …”
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A Framework for Dynamic Network Architecture and Topology Optimization von Shams Shafigh, Alireza, Lorenzo, Beatriz, Glisic, Savo, Perez-Romero, Jordi, DaSilva, Luiz A., MacKenzie, Allen B., Juha Roning

“… A new paradigm in wireless network access is presented and analyzed. In this concept, certain classes of wireless terminals can be turned temporarily into an access point (AP …”
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Neural Network Architecture for Cognitive Navigation in Dynamic Environments von Villacorta-Atienza, Jose Antonio, Makarov, Valeri A.

“… tasks. CIR is a specific cognitive map that compacts time-evolving situations into static structures containing information necessary for navigation …”
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An Online Approach to Estimate Parameters of Phase-Type Distributions von Buchholz, Peter, Dohndorf, Iryna, Kriege, Jan

“… ), a widely used class of distributions in performance and dependability modeling. EM algorithms are typical offline algorithms because they improve the likelihood function by iteratively running through a fixed sample …”
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Scalable Proximal Jacobian Iteration Method With Global Convergence Analysis for Nonconvex Unconstrained Composite Optimizations von Zhang, Hengmin, Qian, Jianjun, Gao, Junbin, Yang, Jian, Xu, Chunyan

“… >-norm and rank function minimization problems. However, due to the absence of convexity in these nonconvex problems, developing efficient algorithms with convergence guarantee becomes very challenging …”
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Novel Discretized Zeroing Neural Network Models for Time-Varying Optimization Aided With Predictor-Corrector Methods von Kong, Ying, Chen, Xi, Jiang, Yunliang, Sun, Danfeng, Zhang, Jun

“… In this article, we derive the predictor-corrector (PC) methods with three-order convergent precision, together with a class of specific general linear three-step (GLTS) rules provided …”
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PERMANENT DECOMPOSITION ALGORITHM FOR THE COMBINATORIAL OBJECTS GENERATION von Turbal, Y. V., Babych, S. V., Kunanets, N. E.

“… Context. The problem of generating vectors consisting of different representatives of a given set of sets is considered. Such problems arise, in particular, in …”
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Convergence and Rate Analysis of Neural Networks for Sparse Approximation von Balavoine, A., Romberg, J., Rozell, C. J.

“… However, the LCA lacks analysis of its convergence properties, and previous results on neural networks for nonsmooth optimization do not apply to the specifics of the LCA architecture …”
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Gaussian process latent class choice models von Sfeir, Georges, Rodrigues, Filipe, Abou-Zeid, Maya

“… •Integration of machine learning and discrete choice models.•New choice model referred to as Gaussian process latent class choice model …”
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Game Dynamics and Cost of Learning in Heterogeneous 4G Networks von Khan, M. A., Tembine, H., Vasilakos, A. V.

“… of the users that we have experimented on OPNET simulations. Considering a dynamic and uncertain environment where the users and operators have only a numerical value …”
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Decomposition Techniques for Multilayer Perceptron Training von Grippo, Luigi, Manno, Andrea, Sciandrone, Marco

“… We define a wide class of batch learning algorithms for MLP, based on the use of block decomposition techniques in the minimization of the error function …”
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POD acceleration of fully implicit solver for unsteady nonlinear flows and its application on grid architecture von Tromeur-Dervout, D., Vassilevski, Y.

“… This approach is appealing to GRID computing: spare processors may help to improve the numerical efficiency and to manage the computing in a reliable way …”
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Regenerative exact simulation for multiserver job model von Golovin, Alexander, Rumyantsev, Alexander, Chakravarthy, Srinivas

“… However, to apply perfect sampling techniques to specific models, additional techniques are required …”
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Approximation of One-Dimensional Darcy–Brinkman–Forchheimer Model by Physics Informed Deep Learning Feedforward Artificial Neural Network and Finite Element Methods: A Comparative Study von Martinez, Mara, Rao, B. Veena S. N., Mallikarjunaiah, S. M.

“… (or simulated neural networks )—a class of machine learning algorithms—has gained a lot of attention due to its applicability in various science and engineering fields …”
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Certifying the Floating-Point Implementation of an Elementary Function Using Gappa von de Dinechin, Florent, Lauter, C, Melquiond, G

“… High confidence in floating-point programs requires proving numerical properties of final and intermediate values …”
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