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A Novel Regularization Based on the Error Function for Sparse Recovery
Journal of Scientific Computing, 2020Regularization plays an important role in solving ill-posed problems by adding extra information about the desired solution, such as sparsity. Many regularization terms usually involve some vector norms.
Weihong Guo, Y. Lou, Jing Qin, Ming Yan
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Chordal Based Error Function for 3-D Pose-Graph Optimization
IEEE Robotics and Automation Letters, 2020Pose-graph optimization (PGO) is a well-known problem in the robotics community. Optimizing a graph means finding the configuration of the nodes that best satisfies the edges.
Irvin Aloise, G. Grisetti
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Journal of Chemical Physics, 2018
Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories.
Wei Chen +2 more
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Auto-associative neural networks ("autoencoders") present a powerful nonlinear dimensionality reduction technique to mine data-driven collective variables from molecular simulation trajectories.
Wei Chen +2 more
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Ray tomography: Errors and error functions
Journal of Applied Geophysics, 1994Tomography is the inversion of boundary projections to reconstruct the internal characteristics of the medium between the source and detector boreholes. Tomography is used to image the structure of geological formations and localized inhomogenieties. This imaging technique may be applied to either seismic or electromagnetic data, typically recorded as ...
J.C. Santamarina, A.C. Reed
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Error-Robust Functional Dependencies
Fundamenta Informaticae, 2004A database user may be confronted with a relation that contains errors. These errors may result from transmission through a noisy channel, or they may have been added deliberately in order to hide or spoil information. Error-robust functional dependencies provide dependencies that still hold in the case of errors.
Hartmann, Sven +3 more
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Symmetry
Numerous researchers have extensively studied various subfamilies of the bi-univalent function family utilizing special functions. In this paper, we introduce and investigate a new subfamily of bi-univalent functions, which is defined on the symmetric ...
A. Amourah +3 more
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Numerous researchers have extensively studied various subfamilies of the bi-univalent function family utilizing special functions. In this paper, we introduce and investigate a new subfamily of bi-univalent functions, which is defined on the symmetric ...
A. Amourah +3 more
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1995
Abstract In previous chapters we have made use of the sum-of-squares error function, which was motivated primarily by analytical simplicity. There are many other possible choices of error function which can also be considered, depending on the particular application.
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Abstract In previous chapters we have made use of the sum-of-squares error function, which was motivated primarily by analytical simplicity. There are many other possible choices of error function which can also be considered, depending on the particular application.
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Advances in Computational Mathematics, 2009
The Gauss error function of a real variable is defined by \(\text{erf}(x)= {2\over\sqrt{\pi}} \int^x_0 e^{-t^2}\,dt\). Results on the error function may be found e.g., in the well-known monographs by Abramowitz-Stegun (1965), Gradshteyn-Ryzhik (1994), or Luke (1975).
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The Gauss error function of a real variable is defined by \(\text{erf}(x)= {2\over\sqrt{\pi}} \int^x_0 e^{-t^2}\,dt\). Results on the error function may be found e.g., in the well-known monographs by Abramowitz-Stegun (1965), Gradshteyn-Ryzhik (1994), or Luke (1975).
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