Space Efficient Approximation to Maximum Matching Size from Uniform Edge Samples

This paper addresses the problem of Quality of Experience (QoE) monitoring for web browsing. In particular, the inference of common Web QoE metrics such as Speed Index (SI) is investigated. Based on a large dataset collected with open web-measurement platforms on different device-types, a unique feature set is designed and used to estimate the RUMSI - an efficient approximation to SI, with machinelearning based regression and classification approaches. Results indicate that it is possible to estimate the RUMSI accurately, and that in particular, recurrent neural networks are highly suitable for the task, as they capture the network dynamics more precisely.

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Efficient approximation of solutions of parametric linear transport equations by ReLU DNNs

Advances in Computational Mathematics ◽

10.1007/s10444-020-09834-7 ◽

2021 ◽

Vol 47 (1) ◽

Author(s):

Fabian Laakmann ◽

Philipp Petersen

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Initial Conditions ◽

Activation Function ◽

Transport Equations ◽

High Dimensional ◽

Linear Transport ◽

Approximation Rates ◽

Curse Of Dimension ◽

Efficient Approximation

AbstractWe demonstrate that deep neural networks with the ReLU activation function can efficiently approximate the solutions of various types of parametric linear transport equations. For non-smooth initial conditions, the solutions of these PDEs are high-dimensional and non-smooth. Therefore, approximation of these functions suffers from a curse of dimension. We demonstrate that through their inherent compositionality deep neural networks can resolve the characteristic flow underlying the transport equations and thereby allow approximation rates independent of the parameter dimension.

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Efficient Approximation of Ligand Rotational and Translational Entropy Changes upon Binding for Use in MM-PBSA Calculations

Journal of Chemical Information and Modeling ◽

10.1021/acs.jcim.6b00373 ◽

2017 ◽

Vol 57 (2) ◽

pp. 170-189 ◽

Cited By ~ 20

Author(s):

Ido Y. Ben-Shalom ◽

Stefania Pfeiffer-Marek ◽

Karl-Heinz Baringhaus ◽

Holger Gohlke

Keyword(s):

Entropy Changes ◽

Translational Entropy ◽

Efficient Approximation

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An efficient approximation for the Generalized Assignment Problem

Information Processing Letters ◽

10.1016/j.ipl.2006.06.003 ◽

2006 ◽

Vol 100 (4) ◽

pp. 162-166 ◽

Cited By ~ 135

Author(s):

Reuven Cohen ◽

Liran Katzir ◽

Danny Raz

Keyword(s):

Assignment Problem ◽

Generalized Assignment Problem ◽

Generalized Assignment ◽

Efficient Approximation

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An Efficient Approximation Method for Calculating Confidence Level of Negative Survey

Mathematical Problems in Engineering ◽

10.1155/2015/958154 ◽

2015 ◽

Vol 2015 ◽

pp. 1-9 ◽

Cited By ~ 1

Author(s):

Ran Liu ◽

Jinghui Peng ◽

Shanyu Tang

Keyword(s):

Central Limit Theorem ◽

Limit Theorem ◽

Greedy Algorithm ◽

Central Limit ◽

Approximation Method ◽

Confidence Level ◽

Present Method ◽

Bayesian Method ◽

Low Efficiency ◽

Efficient Approximation

The confidence level of negative survey is one of the key scientific problems. The present work uses generation function to analyse the confidence level and uses a greedy algorithm to calculate that, which is used to evaluate the dependable level of negative survey. However, the present method is of low efficiency and complex. This study focuses on an efficient approximation method for calculating the confidence level of negative survey. This approximation method based on central limit theorem and Bayesian method can get the results efficiently.

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