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You searched for +publisher:"University of Texas – Austin" +contributor:("Ghosh, Joydeep"). Showing records 1 – 30 of 32 total matches.

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University of Texas – Austin

1. Bansal, Puneet, active 21st century. Analysis and classification of drift susceptible chemosensory responses.

Degree: MSin Engineering, Electrical and Computer Engineering, 2014, University of Texas – Austin

 This report presents machine learning models that can accurately classify gases by analyzing data from an array of 16 sensors. More specifically, the report presents… (more)

Subjects/Keywords: Ensemble classifier; Gas sensor

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APA (6th Edition):

Bansal, Puneet, a. 2. c. (2014). Analysis and classification of drift susceptible chemosensory responses. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/28532

Chicago Manual of Style (16th Edition):

Bansal, Puneet, active 21st century. “Analysis and classification of drift susceptible chemosensory responses.” 2014. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/28532.

MLA Handbook (7th Edition):

Bansal, Puneet, active 21st century. “Analysis and classification of drift susceptible chemosensory responses.” 2014. Web. 14 Aug 2020.

Vancouver:

Bansal, Puneet a2c. Analysis and classification of drift susceptible chemosensory responses. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/28532.

Council of Science Editors:

Bansal, Puneet a2c. Analysis and classification of drift susceptible chemosensory responses. [Masters Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/28532


University of Texas – Austin

2. Park, Yubin. Privacy-aware publication and utilization of healthcare data.

Degree: PhD, Electrical and Computer Engineering, 2014, University of Texas – Austin

 Open access to health data can bring enormous social and economical benefits. However, such access can also lead to privacy breaches, which may result in… (more)

Subjects/Keywords: Data privacy; Healthcare

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APA (6th Edition):

Park, Y. (2014). Privacy-aware publication and utilization of healthcare data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/26932

Chicago Manual of Style (16th Edition):

Park, Yubin. “Privacy-aware publication and utilization of healthcare data.” 2014. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/26932.

MLA Handbook (7th Edition):

Park, Yubin. “Privacy-aware publication and utilization of healthcare data.” 2014. Web. 14 Aug 2020.

Vancouver:

Park Y. Privacy-aware publication and utilization of healthcare data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2014. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/26932.

Council of Science Editors:

Park Y. Privacy-aware publication and utilization of healthcare data. [Doctoral Dissertation]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/26932


University of Texas – Austin

3. Anderson, Dylan Zachary. Supervised gamma process Poisson factorization.

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

 This thesis develops the supervised gamma process Poisson factorization (S-GPPF) framework, a novel supervised topic model for joint modeling of count matrices and document labels.… (more)

Subjects/Keywords: Supervised topic modeling; Bayesian nonparametric; Gamma process; Poisson factorization

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APA (6th Edition):

Anderson, D. Z. (2015). Supervised gamma process Poisson factorization. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/31866

Chicago Manual of Style (16th Edition):

Anderson, Dylan Zachary. “Supervised gamma process Poisson factorization.” 2015. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/31866.

MLA Handbook (7th Edition):

Anderson, Dylan Zachary. “Supervised gamma process Poisson factorization.” 2015. Web. 14 Aug 2020.

Vancouver:

Anderson DZ. Supervised gamma process Poisson factorization. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/31866.

Council of Science Editors:

Anderson DZ. Supervised gamma process Poisson factorization. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/31866


University of Texas – Austin

4. Li, Xiaoqian. Structural equation modeling of political discussion networks.

Degree: MSin Statistics, Statistics, 2017, University of Texas – Austin

 This study conducts structural equation modeling (SEM) of political discussion networks. It examines multiple relationships between political discussion networks—network size and non-kin composition, political efficacy,… (more)

Subjects/Keywords: Structural equation modeling; Categorical variable

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APA (6th Edition):

Li, X. (2017). Structural equation modeling of political discussion networks. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62804

Chicago Manual of Style (16th Edition):

Li, Xiaoqian. “Structural equation modeling of political discussion networks.” 2017. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/62804.

MLA Handbook (7th Edition):

Li, Xiaoqian. “Structural equation modeling of political discussion networks.” 2017. Web. 14 Aug 2020.

Vancouver:

Li X. Structural equation modeling of political discussion networks. [Internet] [Masters thesis]. University of Texas – Austin; 2017. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/62804.

Council of Science Editors:

Li X. Structural equation modeling of political discussion networks. [Masters Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62804


University of Texas – Austin

5. -7421-5165. Topic modeling via scatter/gather clustering.

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

 Latent variable models such as Latent Dirichlet Allocation provide rich tools for analyzing large document corpora. They can uncover a wide range of hidden information… (more)

Subjects/Keywords: Topic model; Scatter; Gather; Clustering; Browsing; Latent dirichlet allocation

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APA (6th Edition):

-7421-5165. (2015). Topic modeling via scatter/gather clustering. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32316

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7421-5165. “Topic modeling via scatter/gather clustering.” 2015. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/32316.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7421-5165. “Topic modeling via scatter/gather clustering.” 2015. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7421-5165. Topic modeling via scatter/gather clustering. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/32316.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7421-5165. Topic modeling via scatter/gather clustering. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32316

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

6. -4895-7744. A closed-form correlation model of oriented bandpass natural images beyond adjacent responses.

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

 Building natural scene statistical models is crucial for a large set of applications starting from the design of faithful image and video quality metrics to… (more)

Subjects/Keywords: Natural scene statistics; Bandpass natural images; Bivariate correlation models

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APA (6th Edition):

-4895-7744. (2015). A closed-form correlation model of oriented bandpass natural images beyond adjacent responses. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32315

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-4895-7744. “A closed-form correlation model of oriented bandpass natural images beyond adjacent responses.” 2015. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/32315.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-4895-7744. “A closed-form correlation model of oriented bandpass natural images beyond adjacent responses.” 2015. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4895-7744. A closed-form correlation model of oriented bandpass natural images beyond adjacent responses. [Internet] [Masters thesis]. University of Texas – Austin; 2015. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/32315.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-4895-7744. A closed-form correlation model of oriented bandpass natural images beyond adjacent responses. [Masters Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32315

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Author name may be incomplete


University of Texas – Austin

7. Verma, Nishant. Biomarker for tracking progression of Alzheimer's disease in clinical trials.

Degree: PhD, Biomedical Engineering, 2015, University of Texas – Austin

 Currently, there are no treatments available for mitigating the neurological effects of Alzheimer's disease. All clinical trials of disease-modifying treatments, which showed promise in animal… (more)

Subjects/Keywords: Alzheimer's disease; Clinical trials; Alzheimer's Disease Assessment Scale-Cognitive subscale; Item response theory; ADAS-Cog; Cognitive impairment; MCI; Clinical trial efficiency; MCI stage; Prodromal stage; Cerebral atrophy; MR volumes; Automatic tissue segmentation; Biomarkers; Alzheimer’s biomarkers; ADAS-Cog scoring methodology

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APA (6th Edition):

Verma, N. (2015). Biomarker for tracking progression of Alzheimer's disease in clinical trials. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46741

Chicago Manual of Style (16th Edition):

Verma, Nishant. “Biomarker for tracking progression of Alzheimer's disease in clinical trials.” 2015. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/46741.

MLA Handbook (7th Edition):

Verma, Nishant. “Biomarker for tracking progression of Alzheimer's disease in clinical trials.” 2015. Web. 14 Aug 2020.

Vancouver:

Verma N. Biomarker for tracking progression of Alzheimer's disease in clinical trials. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2015. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/46741.

Council of Science Editors:

Verma N. Biomarker for tracking progression of Alzheimer's disease in clinical trials. [Doctoral Dissertation]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/46741

8. -6888-3095. Embodied learning for visual recognition.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin

 The field of visual recognition in recent years has come to rely on large expensively curated and manually labeled "bags of disembodied images". In the… (more)

Subjects/Keywords: Computer vision; Unsupervised learning; Embodied learning

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APA (6th Edition):

-6888-3095. (2017). Embodied learning for visual recognition. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63489

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/63489.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-6888-3095. Embodied learning for visual recognition. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/63489.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-6888-3095. Embodied learning for visual recognition. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/63489

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

9. -0620-9177. Data reduction methods for human decision making and learning.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 The rapidly increasing size of data is becoming a major challenge for both humans and machines to process. While more data means more information and… (more)

Subjects/Keywords: Cognitive bias; Decision making; SVM; Data reduction; Neural networks; Selective training

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APA (6th Edition):

-0620-9177. (2019). Data reduction methods for human decision making and learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2911

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-0620-9177. “Data reduction methods for human decision making and learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/2911.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-0620-9177. “Data reduction methods for human decision making and learning.” 2019. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0620-9177. Data reduction methods for human decision making and learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/2911.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-0620-9177. Data reduction methods for human decision making and learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2911

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Author name may be incomplete


University of Texas – Austin

10. -0333-3981. Model-based dynamic relaying for power system protection under uncertainty.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 Several major cascading outages have involved mis-operation or mis-coordination of protective relays during stressed system conditions that resulted in a vulnerable network. Such stressed conditions… (more)

Subjects/Keywords: Power system protection; Power system fault analysis; Distributed generation; Supervised learning; Mixed-integer programming; Stochastic optimization

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APA (6th Edition):

-0333-3981. (2019). Model-based dynamic relaying for power system protection under uncertainty. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2692

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-0333-3981. “Model-based dynamic relaying for power system protection under uncertainty.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/2692.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-0333-3981. “Model-based dynamic relaying for power system protection under uncertainty.” 2019. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-0333-3981. Model-based dynamic relaying for power system protection under uncertainty. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/2692.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-0333-3981. Model-based dynamic relaying for power system protection under uncertainty. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/2692

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Author name may be incomplete


University of Texas – Austin

11. Zhang, Tong, active 2013. Generation planning using Screening Curve Method.

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

 The Screening Curve Method (SCM) is an intuitive and fast model that estimates the least-cost generation mix for generation planning purposes. As an approximation method,… (more)

Subjects/Keywords: Generation planning; Screening Curve Method; Unit commitment; Economic dispatch; Ancillary services; Planned outage; Forced outage

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APA (6th Edition):

Zhang, Tong, a. 2. (2016). Generation planning using Screening Curve Method. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68262

Chicago Manual of Style (16th Edition):

Zhang, Tong, active 2013. “Generation planning using Screening Curve Method.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/68262.

MLA Handbook (7th Edition):

Zhang, Tong, active 2013. “Generation planning using Screening Curve Method.” 2016. Web. 14 Aug 2020.

Vancouver:

Zhang, Tong a2. Generation planning using Screening Curve Method. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/68262.

Council of Science Editors:

Zhang, Tong a2. Generation planning using Screening Curve Method. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/68262

12. Bhowmik, Avradeep. Learning from aggregated data.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 Data aggregation is ubiquitous in modern life. Due to various reasons like privacy, scalability, robustness, etc., ground truth data is often subjected to aggregation before… (more)

Subjects/Keywords: Machine learning; Data mining; Statistical inference; Aggregated data

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APA (6th Edition):

Bhowmik, A. (2019). Learning from aggregated data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1281

Chicago Manual of Style (16th Edition):

Bhowmik, Avradeep. “Learning from aggregated data.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/1281.

MLA Handbook (7th Edition):

Bhowmik, Avradeep. “Learning from aggregated data.” 2019. Web. 14 Aug 2020.

Vancouver:

Bhowmik A. Learning from aggregated data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/1281.

Council of Science Editors:

Bhowmik A. Learning from aggregated data. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1281

13. -7585-6925. Distributed and dynamic factor modeling of online data.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin

 The domain of data mining and machine learning has expanded rapidly in recent years to include both large-scale distributed and streaming computation. Although many open-source… (more)

Subjects/Keywords: Distributed clustering; Dynamic clustering; Matrix factorization; Co-factorization

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APA (6th Edition):

-7585-6925. (2017). Distributed and dynamic factor modeling of online data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62065

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7585-6925. “Distributed and dynamic factor modeling of online data.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/62065.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7585-6925. “Distributed and dynamic factor modeling of online data.” 2017. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7585-6925. Distributed and dynamic factor modeling of online data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/62065.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7585-6925. Distributed and dynamic factor modeling of online data. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62065

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

14. -7186-6405. Algorithms for sparse and structurally constrained discrete optimization problems in bioinformatics and communications.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin

 Inference from high-dimensional noisy data, the task encountered in a wide range of applications including those in wireless communications and bioinformatics, is often computationally challenging.… (more)

Subjects/Keywords: Sparsity; Structures; Sphere decoding; Haplotype; Quasispecies; Clustering; Matrix factorization; Bioinformatics

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APA (6th Edition):

-7186-6405. (2017). Algorithms for sparse and structurally constrained discrete optimization problems in bioinformatics and communications. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61549

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7186-6405. “Algorithms for sparse and structurally constrained discrete optimization problems in bioinformatics and communications.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/61549.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7186-6405. “Algorithms for sparse and structurally constrained discrete optimization problems in bioinformatics and communications.” 2017. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7186-6405. Algorithms for sparse and structurally constrained discrete optimization problems in bioinformatics and communications. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/61549.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7186-6405. Algorithms for sparse and structurally constrained discrete optimization problems in bioinformatics and communications. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61549

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


University of Texas – Austin

15. Joshi, Shalmali Dilip. Constraint based approaches to interpretable and semi-supervised machine learning.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 Interpretability and Explainability of machine learning algorithms are becoming increasingly important as Machine Learning (ML) systems get widely applied to domains like clinical healthcare, social… (more)

Subjects/Keywords: Interpretable machine learning; Semi-supervised machine learning

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APA (6th Edition):

Joshi, S. D. (2019). Constraint based approaches to interpretable and semi-supervised machine learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/1259

Chicago Manual of Style (16th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/1259.

MLA Handbook (7th Edition):

Joshi, Shalmali Dilip. “Constraint based approaches to interpretable and semi-supervised machine learning.” 2019. Web. 14 Aug 2020.

Vancouver:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/1259.

Council of Science Editors:

Joshi SD. Constraint based approaches to interpretable and semi-supervised machine learning. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/1259


University of Texas – Austin

16. https://orcid.org/0000-0002-2528-6784. Robust deep fusion models for self-driving cars.

Degree: PhD, Electrical and Computer Engineering, 2019, University of Texas – Austin

 Deep learning algorithms have been adopted to various applications like self-driving cars and healthcare for their superb performance. In such fields, trustworthy models are indispensable… (more)

Subjects/Keywords: Machine learning; Deep learning; Robustness; Fusion model

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APA (6th Edition):

https://orcid.org/0000-0002-2528-6784. (2019). Robust deep fusion models for self-driving cars. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/5798

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Chicago Manual of Style (16th Edition):

https://orcid.org/0000-0002-2528-6784. “Robust deep fusion models for self-driving cars.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/5798.

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MLA Handbook (7th Edition):

https://orcid.org/0000-0002-2528-6784. “Robust deep fusion models for self-driving cars.” 2019. Web. 14 Aug 2020.

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Author name may be incomplete

Vancouver:

https://orcid.org/0000-0002-2528-6784. Robust deep fusion models for self-driving cars. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/5798.

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Council of Science Editors:

https://orcid.org/0000-0002-2528-6784. Robust deep fusion models for self-driving cars. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/5798

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University of Texas – Austin

17. -1801-9896. Perceptual monocular depth estimation.

Degree: PhD, Electrical and Computer Engineering, 2020, University of Texas – Austin

 Monocular depth estimation (MDE), which is the task of using a single image to predict scene depths, has gained considerable interest, in large part owing… (more)

Subjects/Keywords: Monocular depth estimation; Natural scene statistics; Depth estimation; Perceptual depth estimation; Bivariate natural scene statistics; Bivariate correlation

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APA (6th Edition):

-1801-9896. (2020). Perceptual monocular depth estimation. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/8461

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-1801-9896. “Perceptual monocular depth estimation.” 2020. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/8461.

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Author name may be incomplete

MLA Handbook (7th Edition):

-1801-9896. “Perceptual monocular depth estimation.” 2020. Web. 14 Aug 2020.

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Author name may be incomplete

Vancouver:

-1801-9896. Perceptual monocular depth estimation. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2020. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/8461.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-1801-9896. Perceptual monocular depth estimation. [Doctoral Dissertation]. University of Texas – Austin; 2020. Available from: http://dx.doi.org/10.26153/tsw/8461

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Author name may be incomplete

18. -4122-6383. Strengthening weak supervision for information retrieval.

Degree: MSin Engineering, Electrical and Computer Engineering, 2019, University of Texas – Austin

 The limited availability of ground truth relevance labels has been a major impediment to the application of supervised machine learning techniques to ad-hoc document retrieval… (more)

Subjects/Keywords: Information retrieval; Machine learning; Deep learning; Weak supervision

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APA (6th Edition):

-4122-6383. (2019). Strengthening weak supervision for information retrieval. (Masters Thesis). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/5480

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Chicago Manual of Style (16th Edition):

-4122-6383. “Strengthening weak supervision for information retrieval.” 2019. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/5480.

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Author name may be incomplete

MLA Handbook (7th Edition):

-4122-6383. “Strengthening weak supervision for information retrieval.” 2019. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-4122-6383. Strengthening weak supervision for information retrieval. [Internet] [Masters thesis]. University of Texas – Austin; 2019. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/5480.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-4122-6383. Strengthening weak supervision for information retrieval. [Masters Thesis]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/5480

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Author name may be incomplete

19. Acharyya, Sreangsu. Learning to rank in supervised and unsupervised settings using convexity and monotonicity.

Degree: PhD, Electrical and Computer Engineering, 2013, University of Texas – Austin

 This dissertation addresses the task of learning to rank, both in the supervised and unsupervised settings, by exploiting the interplay of convex functions, monotonic mappings… (more)

Subjects/Keywords: Learning to rank; Convexity; Monotonicity; Bregman divergence

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APA (6th Edition):

Acharyya, S. (2013). Learning to rank in supervised and unsupervised settings using convexity and monotonicity. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/21154

Chicago Manual of Style (16th Edition):

Acharyya, Sreangsu. “Learning to rank in supervised and unsupervised settings using convexity and monotonicity.” 2013. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/21154.

MLA Handbook (7th Edition):

Acharyya, Sreangsu. “Learning to rank in supervised and unsupervised settings using convexity and monotonicity.” 2013. Web. 14 Aug 2020.

Vancouver:

Acharyya S. Learning to rank in supervised and unsupervised settings using convexity and monotonicity. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2013. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/21154.

Council of Science Editors:

Acharyya S. Learning to rank in supervised and unsupervised settings using convexity and monotonicity. [Doctoral Dissertation]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/21154

20. Koyejo, Oluwasanmi Oluseye. Constrained relative entropy minimization with applications to multitask learning.

Degree: PhD, Electrical and Computer Engineering, 2013, University of Texas – Austin

 This dissertation addresses probabilistic inference via relative entropy minimization subject to expectation constraints. A canonical representation of the solution is determined without the requirement for… (more)

Subjects/Keywords: Relative entropy; Maxent; Multitask learning; Exponential families; Gaussian process

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APA (6th Edition):

Koyejo, O. O. (2013). Constrained relative entropy minimization with applications to multitask learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/20793

Chicago Manual of Style (16th Edition):

Koyejo, Oluwasanmi Oluseye. “Constrained relative entropy minimization with applications to multitask learning.” 2013. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/20793.

MLA Handbook (7th Edition):

Koyejo, Oluwasanmi Oluseye. “Constrained relative entropy minimization with applications to multitask learning.” 2013. Web. 14 Aug 2020.

Vancouver:

Koyejo OO. Constrained relative entropy minimization with applications to multitask learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2013. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/20793.

Council of Science Editors:

Koyejo OO. Constrained relative entropy minimization with applications to multitask learning. [Doctoral Dissertation]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/20793

21. Scaffidi, Salvatore Gregory, III. The smartphone as a data collection device.

Degree: MSin Engineering, Electrical and Computer Engineering, 2013, University of Texas – Austin

 The introduction of mobile devices to the pockets and handbags of people living all over the world has made the practice of mobile computing nearly… (more)

Subjects/Keywords: Mobile application; Software design; Data mining; Smartphone; Android

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APA (6th Edition):

Scaffidi, Salvatore Gregory, I. (2013). The smartphone as a data collection device. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22713

Chicago Manual of Style (16th Edition):

Scaffidi, Salvatore Gregory, III. “The smartphone as a data collection device.” 2013. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/22713.

MLA Handbook (7th Edition):

Scaffidi, Salvatore Gregory, III. “The smartphone as a data collection device.” 2013. Web. 14 Aug 2020.

Vancouver:

Scaffidi, Salvatore Gregory I. The smartphone as a data collection device. [Internet] [Masters thesis]. University of Texas – Austin; 2013. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/22713.

Council of Science Editors:

Scaffidi, Salvatore Gregory I. The smartphone as a data collection device. [Masters Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22713

22. Goodall, Todd Richard. Inspection and evaluation of artifacts in digital video sources.

Degree: PhD, Electrical and Computer Engineering, 2018, University of Texas – Austin

 Streaming digital video content providers such as YouTube, Amazon, Hulu, and Netflix collaborate with production teams to obtain new and old video content. These collaborations… (more)

Subjects/Keywords: Digital video; Artifact detection; VIDMAP; VIDSPECT

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APA (6th Edition):

Goodall, T. R. (2018). Inspection and evaluation of artifacts in digital video sources. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68147

Chicago Manual of Style (16th Edition):

Goodall, Todd Richard. “Inspection and evaluation of artifacts in digital video sources.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/68147.

MLA Handbook (7th Edition):

Goodall, Todd Richard. “Inspection and evaluation of artifacts in digital video sources.” 2018. Web. 14 Aug 2020.

Vancouver:

Goodall TR. Inspection and evaluation of artifacts in digital video sources. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/68147.

Council of Science Editors:

Goodall TR. Inspection and evaluation of artifacts in digital video sources. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68147


University of Texas – Austin

23. Kundu, Debarati. Subjective and objective quality evaluation of synthetic and high dynamic range images.

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

 Recent years have seen a huge growth in the acquisition, transmission, and storage of videos. The visual data consists of both natural scenes as well… (more)

Subjects/Keywords: Subject quality evaluation; Objective image quality algorithms; High dynamic range imaging; Synthetic images; Crowdsourcing

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APA (6th Edition):

Kundu, D. (2016). Subjective and objective quality evaluation of synthetic and high dynamic range images. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/40280

Chicago Manual of Style (16th Edition):

Kundu, Debarati. “Subjective and objective quality evaluation of synthetic and high dynamic range images.” 2016. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/40280.

MLA Handbook (7th Edition):

Kundu, Debarati. “Subjective and objective quality evaluation of synthetic and high dynamic range images.” 2016. Web. 14 Aug 2020.

Vancouver:

Kundu D. Subjective and objective quality evaluation of synthetic and high dynamic range images. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2016. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/40280.

Council of Science Editors:

Kundu D. Subjective and objective quality evaluation of synthetic and high dynamic range images. [Doctoral Dissertation]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/40280


University of Texas – Austin

24. -2993-9663. Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin

 The goal of medical imaging is to acquire and display images of human anatomy and function such that they can be optimally interpreted by a… (more)

Subjects/Keywords: Model observer; Medical image quality; Breast cancer; Breast imaging; Tomosynthesis; Multiple signal detection; Channelized Hotelling observer; Partial least squares

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APA (6th Edition):

-2993-9663. (2017). Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/2453

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Chicago Manual of Style (16th Edition):

-2993-9663. “Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/2453.

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Author name may be incomplete

MLA Handbook (7th Edition):

-2993-9663. “Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer.” 2017. Web. 14 Aug 2020.

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Author name may be incomplete

Vancouver:

-2993-9663. Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/2453.

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Author name may be incomplete

Council of Science Editors:

-2993-9663. Model observer for optimizing digital breast tomosynthesis for detection of multifocal and multicentric breast cancer. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://dx.doi.org/10.26153/tsw/2453

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University of Texas – Austin

25. -8850-4718. Improving next-generation wireless network performance and reliability with deep learning.

Degree: PhD, Electrical and Computer Engineering, 2020, University of Texas – Austin

 A rudimentary question whether machine learning in general, or deep learning in particular, could add to the well-established field of wireless communications, which has been… (more)

Subjects/Keywords: Deep learning; Wireless; Reinforcement learning; Artificial networks; Self-organizing networks; Network intelligence

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APA (6th Edition):

-8850-4718. (2020). Improving next-generation wireless network performance and reliability with deep learning. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/8485

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Chicago Manual of Style (16th Edition):

-8850-4718. “Improving next-generation wireless network performance and reliability with deep learning.” 2020. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://dx.doi.org/10.26153/tsw/8485.

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MLA Handbook (7th Edition):

-8850-4718. “Improving next-generation wireless network performance and reliability with deep learning.” 2020. Web. 14 Aug 2020.

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Vancouver:

-8850-4718. Improving next-generation wireless network performance and reliability with deep learning. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2020. [cited 2020 Aug 14]. Available from: http://dx.doi.org/10.26153/tsw/8485.

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Author name may be incomplete

Council of Science Editors:

-8850-4718. Improving next-generation wireless network performance and reliability with deep learning. [Doctoral Dissertation]. University of Texas – Austin; 2020. Available from: http://dx.doi.org/10.26153/tsw/8485

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26. Chen, Yang-Chi, 1973-. Knowledge-based learning for classification of hyperspectral data.

Degree: PhD, Operations Research and Industrial Engineering, 2007, University of Texas – Austin

 This research focuses on three critical issues related to land cover classification using hyperspectral data: i) robust classification of high dimensional input data; ii) utilization… (more)

Subjects/Keywords: Hyperspectral data; Multispectral photography; Remote sensing

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APA (6th Edition):

Chen, Yang-Chi, 1. (2007). Knowledge-based learning for classification of hyperspectral data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/15971

Chicago Manual of Style (16th Edition):

Chen, Yang-Chi, 1973-. “Knowledge-based learning for classification of hyperspectral data.” 2007. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/15971.

MLA Handbook (7th Edition):

Chen, Yang-Chi, 1973-. “Knowledge-based learning for classification of hyperspectral data.” 2007. Web. 14 Aug 2020.

Vancouver:

Chen, Yang-Chi 1. Knowledge-based learning for classification of hyperspectral data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2007. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/15971.

Council of Science Editors:

Chen, Yang-Chi 1. Knowledge-based learning for classification of hyperspectral data. [Doctoral Dissertation]. University of Texas – Austin; 2007. Available from: http://hdl.handle.net/2152/15971

27. -9476-0526. IEEE 802.11ad V2V-radar : a joint vehicle-to-vehicle communication and automotive radar system.

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

 Proprietary millimeter wave (mmWave) radar technologies are widely used in luxury cars to enable active safety functions such as cruise control and collision avoidance. Vehicle-to-vehicle… (more)

Subjects/Keywords: IEEE 802.11ad; Automotive radar; Vehicular communication; Millimeter wave

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APA (6th Edition):

-9476-0526. (2016). IEEE 802.11ad V2V-radar : a joint vehicle-to-vehicle communication and automotive radar system. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/39444

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Chicago Manual of Style (16th Edition):

-9476-0526. “IEEE 802.11ad V2V-radar : a joint vehicle-to-vehicle communication and automotive radar system.” 2016. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/39444.

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MLA Handbook (7th Edition):

-9476-0526. “IEEE 802.11ad V2V-radar : a joint vehicle-to-vehicle communication and automotive radar system.” 2016. Web. 14 Aug 2020.

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Author name may be incomplete

Vancouver:

-9476-0526. IEEE 802.11ad V2V-radar : a joint vehicle-to-vehicle communication and automotive radar system. [Internet] [Masters thesis]. University of Texas – Austin; 2016. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/39444.

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Author name may be incomplete

Council of Science Editors:

-9476-0526. IEEE 802.11ad V2V-radar : a joint vehicle-to-vehicle communication and automotive radar system. [Masters Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/39444

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28. -5294-4228. Scalable smoothing algorithms for massive graph-structured data.

Degree: PhD, Computer Science, 2017, University of Texas – Austin

 Probabilistically modeling noisy data is a crucial step in virtually all scientific experiments and engineering pipelines. Recent years have seen the rise of several high-throughput… (more)

Subjects/Keywords: Smoothing; Algorithms; False discovery rate; Spatial smoothing; Total variation; Trend filtering

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APA (6th Edition):

-5294-4228. (2017). Scalable smoothing algorithms for massive graph-structured data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61823

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Chicago Manual of Style (16th Edition):

-5294-4228. “Scalable smoothing algorithms for massive graph-structured data.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/61823.

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MLA Handbook (7th Edition):

-5294-4228. “Scalable smoothing algorithms for massive graph-structured data.” 2017. Web. 14 Aug 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-5294-4228. Scalable smoothing algorithms for massive graph-structured data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/61823.

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Author name may be incomplete

Council of Science Editors:

-5294-4228. Scalable smoothing algorithms for massive graph-structured data. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61823

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University of Texas – Austin

29. Agogino, Adrian Kujaneck. Design and control of large collections of learning agents.

Degree: PhD, Electrical and Computer Engineering, 2003, University of Texas – Austin

 The intelligent control of multiple autonomous agents is an important yet difficult task. Previous methods used to address this problem have proved to be either… (more)

Subjects/Keywords: Intelligent agents (Computer software); Artificial intelligence

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APA (6th Edition):

Agogino, A. K. (2003). Design and control of large collections of learning agents. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/424

Chicago Manual of Style (16th Edition):

Agogino, Adrian Kujaneck. “Design and control of large collections of learning agents.” 2003. Doctoral Dissertation, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/424.

MLA Handbook (7th Edition):

Agogino, Adrian Kujaneck. “Design and control of large collections of learning agents.” 2003. Web. 14 Aug 2020.

Vancouver:

Agogino AK. Design and control of large collections of learning agents. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2003. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/424.

Council of Science Editors:

Agogino AK. Design and control of large collections of learning agents. [Doctoral Dissertation]. University of Texas – Austin; 2003. Available from: http://hdl.handle.net/2152/424

30. -1801-9896. Automotive top-view image generation using orthogonally diverging fisheye cameras.

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

 Advanced Driver Assistance Systems in vehicles can be a great assistance to drivers by providing them a quick and easy way to visualize their entire… (more)

Subjects/Keywords: Fisheye; Orthogonally diverging stereo; Virtual view calibration; Scene segmentation

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APA (6th Edition):

-1801-9896. (2016). Automotive top-view image generation using orthogonally diverging fisheye cameras. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/43606

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Chicago Manual of Style (16th Edition):

-1801-9896. “Automotive top-view image generation using orthogonally diverging fisheye cameras.” 2016. Masters Thesis, University of Texas – Austin. Accessed August 14, 2020. http://hdl.handle.net/2152/43606.

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MLA Handbook (7th Edition):

-1801-9896. “Automotive top-view image generation using orthogonally diverging fisheye cameras.” 2016. Web. 14 Aug 2020.

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Author name may be incomplete

Vancouver:

-1801-9896. Automotive top-view image generation using orthogonally diverging fisheye cameras. [Internet] [Masters thesis]. University of Texas – Austin; 2016. [cited 2020 Aug 14]. Available from: http://hdl.handle.net/2152/43606.

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Council of Science Editors:

-1801-9896. Automotive top-view image generation using orthogonally diverging fisheye cameras. [Masters Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/43606

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Author name may be incomplete

[1] [2]

.