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You searched for +publisher:"University of Colorado" +contributor:("Christian Ketelsen"). Showing records 1 – 5 of 5 total matches.

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University of Colorado

1. Mendoza, Cristian Rafael. Rays, Waves, and Separatrices.

Degree: MS, Applied Mathematics, 2015, University of Colorado

 A qualitative study on the ray and wave dynamics of light in optical waveguides with separatrix geometry is presented herein. The thesis attempts to answer… (more)

Subjects/Keywords: Electrical Engineering; Periodically Segmented Waveguides; Ray Analysis; Separatrix; Wave Analysis; Applied Mathematics; Optics; Physics

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APA (6th Edition):

Mendoza, C. R. (2015). Rays, Waves, and Separatrices. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/77

Chicago Manual of Style (16th Edition):

Mendoza, Cristian Rafael. “Rays, Waves, and Separatrices.” 2015. Masters Thesis, University of Colorado. Accessed March 05, 2021. https://scholar.colorado.edu/appm_gradetds/77.

MLA Handbook (7th Edition):

Mendoza, Cristian Rafael. “Rays, Waves, and Separatrices.” 2015. Web. 05 Mar 2021.

Vancouver:

Mendoza CR. Rays, Waves, and Separatrices. [Internet] [Masters thesis]. University of Colorado; 2015. [cited 2021 Mar 05]. Available from: https://scholar.colorado.edu/appm_gradetds/77.

Council of Science Editors:

Mendoza CR. Rays, Waves, and Separatrices. [Masters Thesis]. University of Colorado; 2015. Available from: https://scholar.colorado.edu/appm_gradetds/77


University of Colorado

2. Kannan, Karthik. The Big Picture: Loss Functions at the Dataset Level.

Degree: MS, Computer Science, 2017, University of Colorado

  Loss functions play a key role in machine learning optimization problems. Even with their widespread use throughout the field, selecting a loss function tailored… (more)

Subjects/Keywords: Bregman Divergences; Loss Functions; Machine Learning; Property Elicitation; Computer Sciences

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APA (6th Edition):

Kannan, K. (2017). The Big Picture: Loss Functions at the Dataset Level. (Masters Thesis). University of Colorado. Retrieved from https://scholar.colorado.edu/csci_gradetds/146

Chicago Manual of Style (16th Edition):

Kannan, Karthik. “The Big Picture: Loss Functions at the Dataset Level.” 2017. Masters Thesis, University of Colorado. Accessed March 05, 2021. https://scholar.colorado.edu/csci_gradetds/146.

MLA Handbook (7th Edition):

Kannan, Karthik. “The Big Picture: Loss Functions at the Dataset Level.” 2017. Web. 05 Mar 2021.

Vancouver:

Kannan K. The Big Picture: Loss Functions at the Dataset Level. [Internet] [Masters thesis]. University of Colorado; 2017. [cited 2021 Mar 05]. Available from: https://scholar.colorado.edu/csci_gradetds/146.

Council of Science Editors:

Kannan K. The Big Picture: Loss Functions at the Dataset Level. [Masters Thesis]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/csci_gradetds/146


University of Colorado

3. Folberth, James. Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing.

Degree: PhD, 2018, University of Colorado

  In this dissertation we consider numerical methods for a problem in each of numerical linear algebra, digital signal processing, and image processing for super-resolution… (more)

Subjects/Keywords: cross-ambiguity function; duality; non-negative least-squares; qr factorization; image processing; Applied Mathematics; Applied Statistics

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APA (6th Edition):

Folberth, J. (2018). Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/134

Chicago Manual of Style (16th Edition):

Folberth, James. “Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing.” 2018. Doctoral Dissertation, University of Colorado. Accessed March 05, 2021. https://scholar.colorado.edu/appm_gradetds/134.

MLA Handbook (7th Edition):

Folberth, James. “Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing.” 2018. Web. 05 Mar 2021.

Vancouver:

Folberth J. Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing. [Internet] [Doctoral dissertation]. University of Colorado; 2018. [cited 2021 Mar 05]. Available from: https://scholar.colorado.edu/appm_gradetds/134.

Council of Science Editors:

Folberth J. Fast and Reliable Methods in Numerical Linear Algebra, Signal Processing, and Image Processing. [Doctoral Dissertation]. University of Colorado; 2018. Available from: https://scholar.colorado.edu/appm_gradetds/134


University of Colorado

4. Fox, Alyson Lindsey. Algebraic Multigrid(amg) for Graph Laplacian Linear Systems: Extensions of Amg for Signed, Undirected and Unsigned, Directed Graphs.

Degree: PhD, Applied Mathematics, 2017, University of Colorado

  Relational datasets are often modeled as an unsigned, undirected graph due the nice properties of the resulting graph Laplacian, but information is lost if… (more)

Subjects/Keywords: Algebraic Multigrid; Directed graphs; Graph Laplacians; Gremban's expansion; Signed graphs; Applied Mechanics

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APA (6th Edition):

Fox, A. L. (2017). Algebraic Multigrid(amg) for Graph Laplacian Linear Systems: Extensions of Amg for Signed, Undirected and Unsigned, Directed Graphs. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/96

Chicago Manual of Style (16th Edition):

Fox, Alyson Lindsey. “Algebraic Multigrid(amg) for Graph Laplacian Linear Systems: Extensions of Amg for Signed, Undirected and Unsigned, Directed Graphs.” 2017. Doctoral Dissertation, University of Colorado. Accessed March 05, 2021. https://scholar.colorado.edu/appm_gradetds/96.

MLA Handbook (7th Edition):

Fox, Alyson Lindsey. “Algebraic Multigrid(amg) for Graph Laplacian Linear Systems: Extensions of Amg for Signed, Undirected and Unsigned, Directed Graphs.” 2017. Web. 05 Mar 2021.

Vancouver:

Fox AL. Algebraic Multigrid(amg) for Graph Laplacian Linear Systems: Extensions of Amg for Signed, Undirected and Unsigned, Directed Graphs. [Internet] [Doctoral dissertation]. University of Colorado; 2017. [cited 2021 Mar 05]. Available from: https://scholar.colorado.edu/appm_gradetds/96.

Council of Science Editors:

Fox AL. Algebraic Multigrid(amg) for Graph Laplacian Linear Systems: Extensions of Amg for Signed, Undirected and Unsigned, Directed Graphs. [Doctoral Dissertation]. University of Colorado; 2017. Available from: https://scholar.colorado.edu/appm_gradetds/96


University of Colorado

5. Heavner, Nathan. Building Rank-Revealing Factorizations with Randomization.

Degree: PhD, 2019, University of Colorado

  This thesis describes a set of randomized algorithms for computing rank revealing factorizations of matrices. These algorithms are designed specifically to minimize the amount… (more)

Subjects/Keywords: linear algebra; matrix factorizations; randomization; rank-revealing factorizations; Applied Mathematics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Heavner, N. (2019). Building Rank-Revealing Factorizations with Randomization. (Doctoral Dissertation). University of Colorado. Retrieved from https://scholar.colorado.edu/appm_gradetds/155

Chicago Manual of Style (16th Edition):

Heavner, Nathan. “Building Rank-Revealing Factorizations with Randomization.” 2019. Doctoral Dissertation, University of Colorado. Accessed March 05, 2021. https://scholar.colorado.edu/appm_gradetds/155.

MLA Handbook (7th Edition):

Heavner, Nathan. “Building Rank-Revealing Factorizations with Randomization.” 2019. Web. 05 Mar 2021.

Vancouver:

Heavner N. Building Rank-Revealing Factorizations with Randomization. [Internet] [Doctoral dissertation]. University of Colorado; 2019. [cited 2021 Mar 05]. Available from: https://scholar.colorado.edu/appm_gradetds/155.

Council of Science Editors:

Heavner N. Building Rank-Revealing Factorizations with Randomization. [Doctoral Dissertation]. University of Colorado; 2019. Available from: https://scholar.colorado.edu/appm_gradetds/155

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