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You searched for +publisher:"Georgia Tech" +contributor:("Davenport, Mark A."). Showing records 1 – 12 of 12 total matches.

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Georgia Tech

1. Dedhia, Vaibhav. Scene flow for autonomous navigation.

Degree: MS, Electrical and Computer Engineering, 2018, Georgia Tech

 Today, there are various different paradigms for vision based autonomous navigation: mediated perception approaches that parse an entire scene to make driving decision, a direct… (more)

Subjects/Keywords: Autonomous navigation; CNNs; Computer vision

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APA (6th Edition):

Dedhia, V. (2018). Scene flow for autonomous navigation. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59948

Chicago Manual of Style (16th Edition):

Dedhia, Vaibhav. “Scene flow for autonomous navigation.” 2018. Masters Thesis, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/59948.

MLA Handbook (7th Edition):

Dedhia, Vaibhav. “Scene flow for autonomous navigation.” 2018. Web. 26 Apr 2019.

Vancouver:

Dedhia V. Scene flow for autonomous navigation. [Internet] [Masters thesis]. Georgia Tech; 2018. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/59948.

Council of Science Editors:

Dedhia V. Scene flow for autonomous navigation. [Masters Thesis]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59948


Georgia Tech

2. Kingravi, Hassan. Reduced-set models for improving the training and execution speed of kernel methods.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 This thesis aims to contribute to the area of kernel methods, which are a class of machine learning methods known for their wide applicability and… (more)

Subjects/Keywords: Machine learning; Kernel methods; Reproducing kernel Hilbert spaces; Adaptive control; Manifold learning; Algorithms; Computer algorithms; Kernel functions; Support vector machines

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APA (6th Edition):

Kingravi, H. (2014). Reduced-set models for improving the training and execution speed of kernel methods. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51799

Chicago Manual of Style (16th Edition):

Kingravi, Hassan. “Reduced-set models for improving the training and execution speed of kernel methods.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/51799.

MLA Handbook (7th Edition):

Kingravi, Hassan. “Reduced-set models for improving the training and execution speed of kernel methods.” 2014. Web. 26 Apr 2019.

Vancouver:

Kingravi H. Reduced-set models for improving the training and execution speed of kernel methods. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/51799.

Council of Science Editors:

Kingravi H. Reduced-set models for improving the training and execution speed of kernel methods. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51799


Georgia Tech

3. Zou, Jun. Social computing for personalization and credible information mining using probabilistic graphical models.

Degree: PhD, Electrical and Computer Engineering, 2016, Georgia Tech

 In this dissertation, we address challenging social computing problems in personalized recommender systems and social media information mining. We tap into probabilistic graphical models, including… (more)

Subjects/Keywords: Social computing; Recommender systems; Collaborative filtering; Belief propagation; Probabilistic graphical models

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APA (6th Edition):

Zou, J. (2016). Social computing for personalization and credible information mining using probabilistic graphical models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55646

Chicago Manual of Style (16th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/55646.

MLA Handbook (7th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Web. 26 Apr 2019.

Vancouver:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/55646.

Council of Science Editors:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55646


Georgia Tech

4. Moore, Michael George. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

 The purpose of this work is to improve our ability to extract information from data generated by Poisson and Hawkes processes. Our principal focus is… (more)

Subjects/Keywords: Poisson; Hawkes; Point process; Estimation

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APA (6th Edition):

Moore, M. G. (2018). Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60727

Chicago Manual of Style (16th Edition):

Moore, Michael George. “Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/60727.

MLA Handbook (7th Edition):

Moore, Michael George. “Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis.” 2018. Web. 26 Apr 2019.

Vancouver:

Moore MG. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/60727.

Council of Science Editors:

Moore MG. Maximum likelihood estimation of Poisson and Hawkes processes and extensions to Hawkes process analysis. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60727


Georgia Tech

5. Rizwan, Muhammad. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

 The performance of automatic speech recognition (ASR) system can be enhanced by adaptation of the ASR for a particular speaker or a group of speakers.… (more)

Subjects/Keywords: Speaker adaptation; Adaptive phoneme classification; Deep neural networks; Accent classification

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APA (6th Edition):

Rizwan, M. (2017). Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60171

Chicago Manual of Style (16th Edition):

Rizwan, Muhammad. “Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.” 2017. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/60171.

MLA Handbook (7th Edition):

Rizwan, Muhammad. “Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach.” 2017. Web. 26 Apr 2019.

Vancouver:

Rizwan M. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/60171.

Council of Science Editors:

Rizwan M. Adaptation of hybrid deep neural network-hidden Markov model speech recognition system using a sub-space approach. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/60171

6. Spencer, Timothy Scott. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.

Degree: PhD, Mathematics, 2017, Georgia Tech

 The first part of this dissertation explores the application of dominating operators in harmonic analysis by sparse operators. We present preliminary results on dominating certain… (more)

Subjects/Keywords: Weighted inequalities; Sparse operators; Sparse approximation; 1-bit sensing; Restricted isometry property; Quasi-isometry; Noisy embeddings

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APA (6th Edition):

Spencer, T. S. (2017). Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58730

Chicago Manual of Style (16th Edition):

Spencer, Timothy Scott. “Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.” 2017. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/58730.

MLA Handbook (7th Edition):

Spencer, Timothy Scott. “Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing.” 2017. Web. 26 Apr 2019.

Vancouver:

Spencer TS. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/58730.

Council of Science Editors:

Spencer TS. Weighted inequalities via dyadic operators and a learning theory approach to compressive sensing. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58730

7. Xu, Hongteng. Point process-based modeling and analysis of asynchronous event sequences.

Degree: PhD, Electrical and Computer Engineering, 2017, Georgia Tech

 Real-world interactions among multiple entities, such as user behaviors in social networks, job hunting and hopping, and diseases and their complications, often exhibit self-triggering and… (more)

Subjects/Keywords: Point process; Hawkes process; correcting process; Granger causality; impact function; infectivity network; multi-task learning; Dirichlet mixture model; structural regularizer; disciriminative learning; doubly-censored data; attractiveness model

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APA (6th Edition):

Xu, H. (2017). Point process-based modeling and analysis of asynchronous event sequences. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58690

Chicago Manual of Style (16th Edition):

Xu, Hongteng. “Point process-based modeling and analysis of asynchronous event sequences.” 2017. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/58690.

MLA Handbook (7th Edition):

Xu, Hongteng. “Point process-based modeling and analysis of asynchronous event sequences.” 2017. Web. 26 Apr 2019.

Vancouver:

Xu H. Point process-based modeling and analysis of asynchronous event sequences. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/58690.

Council of Science Editors:

Xu H. Point process-based modeling and analysis of asynchronous event sequences. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58690

8. Charles, Adam Shabti. Dynamics and correlations in sparse signal acquisition.

Degree: PhD, Electrical and Computer Engineering, 2015, Georgia Tech

 One of the most important parts of engineered and biological systems is the ability to acquire and interpret information from the surrounding world accurately and… (more)

Subjects/Keywords: Sparsity; Dynamic filtering; Spatial filtering; Hyperspectral imagery; Compressive sensing; Echo state networks; Short-term memory; Network-based optimization

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APA (6th Edition):

Charles, A. S. (2015). Dynamics and correlations in sparse signal acquisition. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53592

Chicago Manual of Style (16th Edition):

Charles, Adam Shabti. “Dynamics and correlations in sparse signal acquisition.” 2015. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/53592.

MLA Handbook (7th Edition):

Charles, Adam Shabti. “Dynamics and correlations in sparse signal acquisition.” 2015. Web. 26 Apr 2019.

Vancouver:

Charles AS. Dynamics and correlations in sparse signal acquisition. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/53592.

Council of Science Editors:

Charles AS. Dynamics and correlations in sparse signal acquisition. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53592

9. Ahmed, Ali. Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals.

Degree: PhD, Electrical and Computer Engineering, 2013, Georgia Tech

 Low-dimensional signal structures naturally arise in a large set of applications in various fields such as medical imaging, machine learning, signal, and array processing. A… (more)

Subjects/Keywords: Low-rank recovery; Structured randomness; Blind deconvolution; Efficient sampling; Low-dimensional topology; Signal processing

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APA (6th Edition):

Ahmed, A. (2013). Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50226

Chicago Manual of Style (16th Edition):

Ahmed, Ali. “Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals.” 2013. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/50226.

MLA Handbook (7th Edition):

Ahmed, Ali. “Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals.” 2013. Web. 26 Apr 2019.

Vancouver:

Ahmed A. Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/50226.

Council of Science Editors:

Ahmed A. Low-rank matrix recovery: blind deconvolution and efficient sampling of correlated signals. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50226

10. Massimino, Andrew K. Learning to adapt under practical sensing constraints.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

 The purpose of this work is to explore the capability of sensing systems to acquire information adaptively when they are subject to practical measurement constraints.… (more)

Subjects/Keywords: Sparsity; Compressive sensing; Adaptive sensing; Constrained sensing; Optimal experimental design; Pairwise comparisons; Recommender systems; Preferences

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APA (6th Edition):

Massimino, A. K. (2018). Learning to adapt under practical sensing constraints. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60776

Chicago Manual of Style (16th Edition):

Massimino, Andrew K. “Learning to adapt under practical sensing constraints.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/60776.

MLA Handbook (7th Edition):

Massimino, Andrew K. “Learning to adapt under practical sensing constraints.” 2018. Web. 26 Apr 2019.

Vancouver:

Massimino AK. Learning to adapt under practical sensing constraints. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/60776.

Council of Science Editors:

Massimino AK. Learning to adapt under practical sensing constraints. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60776

11. Parrish, Nathan VerDon. System configuration for proportional control of an assistive technology for patients with cervical spinal cord injuries.

Degree: PhD, Electrical and Computer Engineering, 2018, Georgia Tech

 Prior Work in the GTBionics Lab at Georgia Tech has lead to the design of an assistive technology device for people with quadriplegia in the… (more)

Subjects/Keywords: Magnetometer; Sensor calibration; Assistive technology; Spinal cord injury; Tongue

…infrastructure. To this end, work has been done at Georgia Tech to create an assistive technology… …of work done in developing this system at the GTBionics lab at Georgia Tech. Past research… 

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APA (6th Edition):

Parrish, N. V. (2018). System configuration for proportional control of an assistive technology for patients with cervical spinal cord injuries. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60779

Chicago Manual of Style (16th Edition):

Parrish, Nathan VerDon. “System configuration for proportional control of an assistive technology for patients with cervical spinal cord injuries.” 2018. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/60779.

MLA Handbook (7th Edition):

Parrish, Nathan VerDon. “System configuration for proportional control of an assistive technology for patients with cervical spinal cord injuries.” 2018. Web. 26 Apr 2019.

Vancouver:

Parrish NV. System configuration for proportional control of an assistive technology for patients with cervical spinal cord injuries. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/60779.

Council of Science Editors:

Parrish NV. System configuration for proportional control of an assistive technology for patients with cervical spinal cord injuries. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60779

12. Balavoine, Aurele. Mathematical analysis of a dynamical system for sparse recovery.

Degree: PhD, Electrical and Computer Engineering, 2014, Georgia Tech

 This thesis presents the mathematical analysis of a continuous-times system for sparse signal recovery. Sparse recovery arises in Compressed Sensing (CS), where signals of large… (more)

Subjects/Keywords: Sparse recovery; Neural network; L1-minimization; Nonsmooth optimization; Compressed sensing; Tracking; ISTA; LCA; Sparse matrices; Signal processing Digital techniques; Mathematical optimization

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APA (6th Edition):

Balavoine, A. (2014). Mathematical analysis of a dynamical system for sparse recovery. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/51882

Chicago Manual of Style (16th Edition):

Balavoine, Aurele. “Mathematical analysis of a dynamical system for sparse recovery.” 2014. Doctoral Dissertation, Georgia Tech. Accessed April 26, 2019. http://hdl.handle.net/1853/51882.

MLA Handbook (7th Edition):

Balavoine, Aurele. “Mathematical analysis of a dynamical system for sparse recovery.” 2014. Web. 26 Apr 2019.

Vancouver:

Balavoine A. Mathematical analysis of a dynamical system for sparse recovery. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1853/51882.

Council of Science Editors:

Balavoine A. Mathematical analysis of a dynamical system for sparse recovery. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/51882

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