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You searched for +publisher:"University of Texas – Austin" +contributor:("Dimakis, Alexandros G."). Showing records 1 – 12 of 12 total matches.

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University of Texas – Austin

1. -5777-2824. A study of generative adversarial networks and possible extensions of GANs.

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

 The goal of our research is to explore the power of generative adversarial networks (GANs). We take a review of deep learning and many extended… (more)

Subjects/Keywords: Deep learning; Generative adversarial networks

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APA (6th Edition):

-5777-2824. (2017). A study of generative adversarial networks and possible extensions of GANs. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61664

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 (16th Edition):

-5777-2824. “A study of generative adversarial networks and possible extensions of GANs.” 2017. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/61664.

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 (7th Edition):

-5777-2824. “A study of generative adversarial networks and possible extensions of GANs.” 2017. Web. 25 Mar 2019.

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

Vancouver:

-5777-2824. A study of generative adversarial networks and possible extensions of GANs. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/61664.

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:

-5777-2824. A study of generative adversarial networks and possible extensions of GANs. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61664

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

2. Chucri, Samer Gerges. Image compression using locally sensitive hashing.

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

 The problem of archiving photos is becoming increasingly important as image databases are growing more popular, and larger in size. One could take the example… (more)

Subjects/Keywords: Image compression; Locally sensitive hashing; Compression algorithms

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APA (6th Edition):

Chucri, S. G. (2013). Image compression using locally sensitive hashing. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/22742

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Chucri, Samer Gerges. “Image compression using locally sensitive hashing.” 2013. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/22742.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Chucri, Samer Gerges. “Image compression using locally sensitive hashing.” 2013. Web. 25 Mar 2019.

Vancouver:

Chucri SG. Image compression using locally sensitive hashing. [Internet] [Thesis]. University of Texas – Austin; 2013. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/22742.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Chucri SG. Image compression using locally sensitive hashing. [Thesis]. University of Texas – Austin; 2013. Available from: http://hdl.handle.net/2152/22742

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

3. Papailiopoulos, Dimitrios. Distributed large-scale data storage and processing.

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

 This thesis makes progress towards the fundamental understanding of heterogeneous and dynamic information systems and the way that we store and process massive data-sets. Reliable… (more)

Subjects/Keywords: Codes for distributed storage; Big-graph analytics

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APA (6th Edition):

Papailiopoulos, D. (2014). Distributed large-scale data storage and processing. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/29145

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Papailiopoulos, Dimitrios. “Distributed large-scale data storage and processing.” 2014. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/29145.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Papailiopoulos, Dimitrios. “Distributed large-scale data storage and processing.” 2014. Web. 25 Mar 2019.

Vancouver:

Papailiopoulos D. Distributed large-scale data storage and processing. [Internet] [Thesis]. University of Texas – Austin; 2014. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/29145.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Papailiopoulos D. Distributed large-scale data storage and processing. [Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/29145

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

4. Tandon, Rashish. On structured and distributed learning.

Degree: Computer Sciences, 2018, University of Texas – Austin

 With the growth in size and complexity of data, methods exploiting low-dimensional structure, as well as distributed methods, have been playing an ever important role… (more)

Subjects/Keywords: Machine learning; Graphical models; Ising models; Neighborhood selection; Regression; Linear regression; Kernel ridge regression; Local learning; Stragglers; Coding theory; Gradient coding

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APA (6th Edition):

Tandon, R. (2018). On structured and distributed learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68175

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Tandon, Rashish. “On structured and distributed learning.” 2018. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/68175.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Tandon, Rashish. “On structured and distributed learning.” 2018. Web. 25 Mar 2019.

Vancouver:

Tandon R. On structured and distributed learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/68175.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Tandon R. On structured and distributed learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68175

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

5. Khanna, Rajiv Ashu. New perspectives and applications for greedy algorithms in machine learning.

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

 Approximating probability densities is a core problem in Bayesian statistics, where the inference involves the computation of a posterior distribution. Variational Inference (VI) is a… (more)

Subjects/Keywords: Approximate inference; Submodularity

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APA (6th Edition):

Khanna, R. A. (2018). New perspectives and applications for greedy algorithms in machine learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/69183

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Khanna, Rajiv Ashu. “New perspectives and applications for greedy algorithms in machine learning.” 2018. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/69183.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Khanna, Rajiv Ashu. “New perspectives and applications for greedy algorithms in machine learning.” 2018. Web. 25 Mar 2019.

Vancouver:

Khanna RA. New perspectives and applications for greedy algorithms in machine learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/69183.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Khanna RA. New perspectives and applications for greedy algorithms in machine learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/69183

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

6. -3269-6167. Graph analytics and subset selection problems in machine learning.

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

 In this dissertation we examine two topics relevant to modern machine learning research: 1) Subgraph counting and 2) High-dimensional subset selection. The former can be… (more)

Subjects/Keywords: Machine learning; Approximation algorithms; Graph analytics; Graph algorithms; Subset selection; Submodular optimization; Weak submodularity; Restricted strong convexity; Streaming algorithms; Interpretability

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APA (6th Edition):

-3269-6167. (2018). Graph analytics and subset selection problems in machine learning. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68499

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 (16th Edition):

-3269-6167. “Graph analytics and subset selection problems in machine learning.” 2018. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/68499.

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 (7th Edition):

-3269-6167. “Graph analytics and subset selection problems in machine learning.” 2018. Web. 25 Mar 2019.

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

Vancouver:

-3269-6167. Graph analytics and subset selection problems in machine learning. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/68499.

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:

-3269-6167. Graph analytics and subset selection problems in machine learning. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68499

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

7. Kocaoglu, Murat. Causality : from learning to generative models.

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

 Causality is a fundamental concept in multiple disciplines. Causal questions arise in fields ranging from medical research to engineering, philosophy to physics. The last few… (more)

Subjects/Keywords: Causality; Learning; Generative adversarial networks; Entropic

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APA (6th Edition):

Kocaoglu, M. (2018). Causality : from learning to generative models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68646

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Kocaoglu, Murat. “Causality : from learning to generative models.” 2018. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/68646.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Kocaoglu, Murat. “Causality : from learning to generative models.” 2018. Web. 25 Mar 2019.

Vancouver:

Kocaoglu M. Causality : from learning to generative models. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/68646.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Kocaoglu M. Causality : from learning to generative models. [Thesis]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68646

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

8. -9790-6500. New coding techniques for distributed storage systems: enabling locality, availability and security.

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

 Distributed storage systems (a.k.a. cloud storage networks) are becoming increasingly important, given the need to put away vast amounts of data that are being generated,… (more)

Subjects/Keywords: Coding for distributed storage; Locally repairable codes; Regenerating codes; Code repair; Cooperative repair; Secure distributed storage

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APA (6th Edition):

-9790-6500. (2015). New coding techniques for distributed storage systems: enabling locality, availability and security. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/32413

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 (16th Edition):

-9790-6500. “New coding techniques for distributed storage systems: enabling locality, availability and security.” 2015. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/32413.

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 (7th Edition):

-9790-6500. “New coding techniques for distributed storage systems: enabling locality, availability and security.” 2015. Web. 25 Mar 2019.

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

Vancouver:

-9790-6500. New coding techniques for distributed storage systems: enabling locality, availability and security. [Internet] [Thesis]. University of Texas – Austin; 2015. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/32413.

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:

-9790-6500. New coding techniques for distributed storage systems: enabling locality, availability and security. [Thesis]. University of Texas – Austin; 2015. Available from: http://hdl.handle.net/2152/32413

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

9. Asteris, Megasthenis. Quadratic maximization under combinatorial constraints and related applications.

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

 Motivated primarily by restricted variants of Principal Component Analysis (PCA), we study quadratic maximization problems subject to sparsity, nonnegativity and other combinatorial constraints. Intuitively, a… (more)

Subjects/Keywords: Quadratic maximization; Sparse nonnegative PCA

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APA (6th Edition):

Asteris, M. (2016). Quadratic maximization under combinatorial constraints and related applications. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46455

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Asteris, Megasthenis. “Quadratic maximization under combinatorial constraints and related applications.” 2016. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/46455.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Asteris, Megasthenis. “Quadratic maximization under combinatorial constraints and related applications.” 2016. Web. 25 Mar 2019.

Vancouver:

Asteris M. Quadratic maximization under combinatorial constraints and related applications. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/46455.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Asteris M. Quadratic maximization under combinatorial constraints and related applications. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46455

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

10. -7845-7631. Graph theoretic results on index coding, causal inference and learning graphical models.

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

 Exploiting and learning graph structures is becoming ubiquitous in Network Information Theory and Machine Learning. The former deals with efficient communication schemes in a many-node… (more)

Subjects/Keywords: Index coding; Causal inference; Information theory; Graphical models

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APA (6th Edition):

-7845-7631. (2016). Graph theoretic results on index coding, causal inference and learning graphical models. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/44041

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 (16th Edition):

-7845-7631. “Graph theoretic results on index coding, causal inference and learning graphical models.” 2016. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/44041.

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 (7th Edition):

-7845-7631. “Graph theoretic results on index coding, causal inference and learning graphical models.” 2016. Web. 25 Mar 2019.

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

Vancouver:

-7845-7631. Graph theoretic results on index coding, causal inference and learning graphical models. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/44041.

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:

-7845-7631. Graph theoretic results on index coding, causal inference and learning graphical models. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/44041

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

11. Park, Dohyung. Efficient non-convex algorithms for large-scale learning problems.

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

 The emergence of modern large-scale datasets has led to a huge interest in the problem of learning hidden complex structures. Not only can models from… (more)

Subjects/Keywords: Machine learning; Non-convex optimization

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

APA (6th Edition):

Park, D. (2016). Efficient non-convex algorithms for large-scale learning problems. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/46581

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Park, Dohyung. “Efficient non-convex algorithms for large-scale learning problems.” 2016. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/46581.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Park, Dohyung. “Efficient non-convex algorithms for large-scale learning problems.” 2016. Web. 25 Mar 2019.

Vancouver:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Internet] [Thesis]. University of Texas – Austin; 2016. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/46581.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Park D. Efficient non-convex algorithms for large-scale learning problems. [Thesis]. University of Texas – Austin; 2016. Available from: http://hdl.handle.net/2152/46581

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

12. -5991-6641. Modeling and analyzing device-to-device content distribution in cellular networks.

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

 Device-to-device (D2D) communication is a promising approach to optimize the utilization of air interface resources in 5G networks, since it allows decentralized proximity-based communication. To… (more)

Subjects/Keywords: Content distribution; Caching; Device-to-device; Spatial diversity

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APA (6th Edition):

-5991-6641. (2017). Modeling and analyzing device-to-device content distribution in cellular networks. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/67991

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 (16th Edition):

-5991-6641. “Modeling and analyzing device-to-device content distribution in cellular networks.” 2017. Thesis, University of Texas – Austin. Accessed March 25, 2019. http://hdl.handle.net/2152/67991.

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 (7th Edition):

-5991-6641. “Modeling and analyzing device-to-device content distribution in cellular networks.” 2017. Web. 25 Mar 2019.

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

Vancouver:

-5991-6641. Modeling and analyzing device-to-device content distribution in cellular networks. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Mar 25]. Available from: http://hdl.handle.net/2152/67991.

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:

-5991-6641. Modeling and analyzing device-to-device content distribution in cellular networks. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/67991

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Not specified: Masters Thesis or Doctoral Dissertation

.