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You searched for `+publisher:"University of Texas – Austin" +contributor:("Ward, Rachel")`

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University of Texas – Austin

1. Le, Ellen Brooke. Data-driven reduction strategies for Bayesian inverse problems.

Degree: Computational Science, Engineering, and Mathematics, 2018, University of Texas – Austin

URL: http://hdl.handle.net/2152/65851

► A persistent central challenge in computational science and engineering (CSE), with both national and global security implications, is the efficient solution of large-scale Bayesian inverse…
(more)

Subjects/Keywords: Bayesian inverse problems

Record Details Similar Records

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

APA (6^{th} Edition):

Le, E. B. (2018). Data-driven reduction strategies for Bayesian inverse problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65851

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Le, Ellen Brooke. “Data-driven reduction strategies for Bayesian inverse problems.” 2018. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/65851.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Le, Ellen Brooke. “Data-driven reduction strategies for Bayesian inverse problems.” 2018. Web. 21 Apr 2019.

Vancouver:

Le EB. Data-driven reduction strategies for Bayesian inverse problems. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/65851.

Note: this citation may be lacking information needed for this citation format:

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Le EB. Data-driven reduction strategies for Bayesian inverse problems. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65851

Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

2. -4555-7796. Labeling and denoising geometrically parameterized data with applications to Cryo-EM.

Degree: Mathematics, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/31518

► Many data sets encountered in practice depend continuously on a geometric parameter. An important example of this is image collections from Cryo-EM experiments, where the…
(more)

Subjects/Keywords: Applied math; Cryo electron microscopy

Record Details Similar Records

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

APA (6^{th} Edition):

-4555-7796. (2015). Labeling and denoising geometrically parameterized data with applications to Cryo-EM. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31518

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

-4555-7796. “Labeling and denoising geometrically parameterized data with applications to Cryo-EM.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/31518.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

-4555-7796. “Labeling and denoising geometrically parameterized data with applications to Cryo-EM.” 2015. Web. 21 Apr 2019.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Vancouver:

-4555-7796. Labeling and denoising geometrically parameterized data with applications to Cryo-EM. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/31518.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-4555-7796. Labeling and denoising geometrically parameterized data with applications to Cryo-EM. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31518

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

3. -7606-1366. Provable non-convex optimization for learning parametric models.

Degree: Computational Science, Engineering, and Mathematics, 2018, University of Texas – Austin

URL: http://hdl.handle.net/2152/69011

► Non-convex optimization plays an important role in recent advances of machine learning. A large number of machine learning tasks are performed by solving a non-convex…
(more)

Subjects/Keywords: Numerical optimization; Machine learning; Statistics

Record Details Similar Records

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

APA (6^{th} Edition):

-7606-1366. (2018). Provable non-convex optimization for learning parametric models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/69011

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

-7606-1366. “Provable non-convex optimization for learning parametric models.” 2018. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/69011.

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

-7606-1366. “Provable non-convex optimization for learning parametric models.” 2018. Web. 21 Apr 2019.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Vancouver:

-7606-1366. Provable non-convex optimization for learning parametric models. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/69011.

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-7606-1366. Provable non-convex optimization for learning parametric models. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/69011

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

4. Yi, Xinyang. Learning with latent structures, robustness and non-linearity : non-convex approaches.

Degree: Electrical and Computer Engineering, 2016, University of Texas – Austin

URL: http://hdl.handle.net/2152/46474

► Non-convex optimization based algorithms are ubiquitous in machine learning and statistical estimation, especially in dealing with complex models that are noisy, non-linear or contain latent…
(more)

Subjects/Keywords: Statistical machine learning; High dimensional statistics; Non-convex optimization; Mixed linear regression

Record Details Similar Records

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

APA (6^{th} Edition):

Yi, X. (2016). Learning with latent structures, robustness and non-linearity : non-convex approaches. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46474

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Yi, Xinyang. “Learning with latent structures, robustness and non-linearity : non-convex approaches.” 2016. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/46474.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Yi, Xinyang. “Learning with latent structures, robustness and non-linearity : non-convex approaches.” 2016. Web. 21 Apr 2019.

Vancouver:

Yi X. Learning with latent structures, robustness and non-linearity : non-convex approaches. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/46474.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Yi X. Learning with latent structures, robustness and non-linearity : non-convex approaches. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46474

Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

5. -4968-3829. Relax, descend and certify : optimization techniques for typically tractable data problems.

Degree: Mathematics, 2017, University of Texas – Austin

URL: http://hdl.handle.net/2152/62104

► In this thesis we explore different mathematical techniques for extracting information from data. In particular we focus in machine learning problems such as clustering and…
(more)

Subjects/Keywords: Optimization; Data science

Record Details Similar Records

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

APA (6^{th} Edition):

-4968-3829. (2017). Relax, descend and certify : optimization techniques for typically tractable data problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62104

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

-4968-3829. “Relax, descend and certify : optimization techniques for typically tractable data problems.” 2017. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/62104.

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

-4968-3829. “Relax, descend and certify : optimization techniques for typically tractable data problems.” 2017. Web. 21 Apr 2019.

Note: this citation may be lacking information needed for this citation format:

Author name may be incomplete

Vancouver:

-4968-3829. Relax, descend and certify : optimization techniques for typically tractable data problems. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/62104.

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-4968-3829. Relax, descend and certify : optimization techniques for typically tractable data problems. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62104

Author name may be incomplete

Not specified: Masters Thesis or Doctoral Dissertation

University of Texas – Austin

6. Bhojanapalli, Venkata Sesha Pavana Srinadh. Large scale matrix factorization with guarantees: sampling and bi-linearity.

Degree: Electrical and Computer Engineering, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/32832

► Low rank matrix factorization is an important step in many high dimensional machine learning algorithms. Traditional algorithms for factorization do not scale well with the…
(more)

Subjects/Keywords: Matrix completion; Non-convex optimization; Low rank approximation; Semi-definite optimization; Tensor factorization; Scalable algorithms

Record Details Similar Records

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

APA (6^{th} Edition):

Bhojanapalli, V. S. P. S. (2015). Large scale matrix factorization with guarantees: sampling and bi-linearity. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32832

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/32832.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Bhojanapalli, Venkata Sesha Pavana Srinadh. “Large scale matrix factorization with guarantees: sampling and bi-linearity.” 2015. Web. 21 Apr 2019.

Vancouver:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/32832.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Bhojanapalli VSPS. Large scale matrix factorization with guarantees: sampling and bi-linearity. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32832

Not specified: Masters Thesis or Doctoral Dissertation

7. Lopez, Oscar Fabian. A compressive sensing approach to solving nonograms.

Degree: Mathematics, 2013, University of Texas – Austin

URL: http://hdl.handle.net/2152/22669

► A nonogram is a logic puzzle where one shades certain cells of a 2D grid to reveal a hidden image. One uses the sequences of…
(more)

Subjects/Keywords: Nonogram; Compressive sensing; Binary integer programming

Record Details Similar Records

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

APA (6^{th} Edition):

Lopez, O. F. (2013). A compressive sensing approach to solving nonograms. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22669

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Lopez, Oscar Fabian. “A compressive sensing approach to solving nonograms.” 2013. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/22669.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Lopez, Oscar Fabian. “A compressive sensing approach to solving nonograms.” 2013. Web. 21 Apr 2019.

Vancouver:

Lopez OF. A compressive sensing approach to solving nonograms. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/22669.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Lopez OF. A compressive sensing approach to solving nonograms. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22669

Not specified: Masters Thesis or Doctoral Dissertation

8. Knudson, Karin Comer. Recovery of continuous quantities from discrete and binary data with applications to neural data.

Degree: Mathematics, 2014, University of Texas – Austin

URL: http://hdl.handle.net/2152/28424

► We consider three problems, motivated by questions in computational neuroscience, related to recovering continuous quantities from binary or discrete data or measurements in the context…
(more)

Subjects/Keywords: Compressive sensing; Computational neuroscience; Signal processing; Spike sorting; Machine learning

Record Details Similar Records

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

APA (6^{th} Edition):

Knudson, K. C. (2014). Recovery of continuous quantities from discrete and binary data with applications to neural data. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28424

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Knudson, Karin Comer. “Recovery of continuous quantities from discrete and binary data with applications to neural data.” 2014. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/28424.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Knudson, Karin Comer. “Recovery of continuous quantities from discrete and binary data with applications to neural data.” 2014. Web. 21 Apr 2019.

Vancouver:

Knudson KC. Recovery of continuous quantities from discrete and binary data with applications to neural data. [Internet] [Thesis]. University of Texas – Austin; 2014. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/28424.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Knudson KC. Recovery of continuous quantities from discrete and binary data with applications to neural data. [Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28424

Not specified: Masters Thesis or Doctoral Dissertation

9. Jo, Jason. Structured low complexity data mining.

Degree: Mathematics, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/31510

► Due to the rapidly increasing dimensionality of modern datasets many classical approximation algorithms have run into severe computational bottlenecks. This has often been referred to…
(more)

Subjects/Keywords: Greedy sparse approximation; Weighted matrix completion

Record Details Similar Records

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

APA (6^{th} Edition):

Jo, J. (2015). Structured low complexity data mining. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31510

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Jo, Jason. “Structured low complexity data mining.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/31510.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Jo, Jason. “Structured low complexity data mining.” 2015. Web. 21 Apr 2019.

Vancouver:

Jo J. Structured low complexity data mining. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/31510.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jo J. Structured low complexity data mining. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31510

Not specified: Masters Thesis or Doctoral Dissertation

10. Vallélian, Sarah Catherine. Quantitative PAT with unknown ultrasound speed : uncertainty characterization and reconstruction methods.

Degree: Mathematics, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/39249

► Quantitative photoacoustic tomography (QPAT) is a hybrid medical imaging modality that combines high-resolution ultrasound tomography with high-contrast optical tomography. The objective of QPAT is to…
(more)

Subjects/Keywords: Photoacoustic tomography; Inverse problems; Hybrid inverse problems; Image reconstruction; One-step reconstruction; Numerical optimization; Uncertainty quantification; Internal data; Acoustic wave equation; Diffusion equation

Record Details Similar Records

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

APA (6^{th} Edition):

Vallélian, S. C. (2015). Quantitative PAT with unknown ultrasound speed : uncertainty characterization and reconstruction methods. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39249

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

Vallélian, Sarah Catherine. “Quantitative PAT with unknown ultrasound speed : uncertainty characterization and reconstruction methods.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/39249.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

Vallélian, Sarah Catherine. “Quantitative PAT with unknown ultrasound speed : uncertainty characterization and reconstruction methods.” 2015. Web. 21 Apr 2019.

Vancouver:

Vallélian SC. Quantitative PAT with unknown ultrasound speed : uncertainty characterization and reconstruction methods. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/39249.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vallélian SC. Quantitative PAT with unknown ultrasound speed : uncertainty characterization and reconstruction methods. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/39249

Not specified: Masters Thesis or Doctoral Dissertation

11. White, Christopher Dale. Optimality guarantees for non-convex low rank matrix recovery problems.

Degree: Mathematics, 2015, University of Texas – Austin

URL: http://hdl.handle.net/2152/32534

► Low rank matrices lie at the heart of many techniques in scientific computing and machine learning. In this thesis, we examine various scenarios in which…
(more)

Subjects/Keywords: Optimization; Non-convex; Low rank matrix

Record Details Similar Records

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

APA (6^{th} Edition):

White, C. D. (2015). Optimality guarantees for non-convex low rank matrix recovery problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32534

Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16^{th} Edition):

White, Christopher Dale. “Optimality guarantees for non-convex low rank matrix recovery problems.” 2015. Thesis, University of Texas – Austin. Accessed April 21, 2019. http://hdl.handle.net/2152/32534.

Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7^{th} Edition):

White, Christopher Dale. “Optimality guarantees for non-convex low rank matrix recovery problems.” 2015. Web. 21 Apr 2019.

Vancouver:

White CD. Optimality guarantees for non-convex low rank matrix recovery problems. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Apr 21]. Available from: http://hdl.handle.net/2152/32534.

Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

White CD. Optimality guarantees for non-convex low rank matrix recovery problems. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32534

Not specified: Masters Thesis or Doctoral Dissertation