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You searched for +publisher:"Georgia Tech" +contributor:("Boots, Byron"). Showing records 1 – 23 of 23 total matches.

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1. Singhal, Prateek. Multimodal tracking for robust pose estimation.

Degree: MS, Computer Science, 2016, Georgia Tech

 An on-line 3D visual object tracking framework for monocular cameras by incorporating spatial knowledge and uncertainty from semantic mapping along with high frequency measurements from… (more)

Subjects/Keywords: SLAM; Tracking; Vision

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APA (6th Edition):

Singhal, P. (2016). Multimodal tracking for robust pose estimation. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/54970

Chicago Manual of Style (16th Edition):

Singhal, Prateek. “Multimodal tracking for robust pose estimation.” 2016. Masters Thesis, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/54970.

MLA Handbook (7th Edition):

Singhal, Prateek. “Multimodal tracking for robust pose estimation.” 2016. Web. 05 Mar 2021.

Vancouver:

Singhal P. Multimodal tracking for robust pose estimation. [Internet] [Masters thesis]. Georgia Tech; 2016. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/54970.

Council of Science Editors:

Singhal P. Multimodal tracking for robust pose estimation. [Masters Thesis]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/54970

2. Dai, Bo. Learning over functions, distributions and dynamics via stochastic optimization.

Degree: PhD, Computational Science and Engineering, 2018, Georgia Tech

 Machine learning has recently witnessed revolutionary success in a wide spectrum of domains. The learning objectives, model representation, and learning algorithms are important components of… (more)

Subjects/Keywords: Nonparametric method; Stochastic optimization; Reproducing kernel Hilbert space (RKHS); Functional gradient; Bayesian inference; Monte-Carlo approximation; Fenchel's duality; Saddle-point problem; Reinforcement learning; Markov decision process; Bellman equation

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APA (6th Edition):

Dai, B. (2018). Learning over functions, distributions and dynamics via stochastic optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60316

Chicago Manual of Style (16th Edition):

Dai, Bo. “Learning over functions, distributions and dynamics via stochastic optimization.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/60316.

MLA Handbook (7th Edition):

Dai, Bo. “Learning over functions, distributions and dynamics via stochastic optimization.” 2018. Web. 05 Mar 2021.

Vancouver:

Dai B. Learning over functions, distributions and dynamics via stochastic optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/60316.

Council of Science Editors:

Dai B. Learning over functions, distributions and dynamics via stochastic optimization. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60316


Georgia Tech

3. Williams, Grady Robert. Model predictive path integral control: Theoretical foundations and applications to autonomous driving.

Degree: PhD, Computer Science, 2019, Georgia Tech

 This thesis presents a new approach for stochastic model predictive (optimal) control: model predictive path integral control, which is based on massive parallel sampling of… (more)

Subjects/Keywords: Stochastic optimal control; Autonomous driving

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APA (6th Edition):

Williams, G. R. (2019). Model predictive path integral control: Theoretical foundations and applications to autonomous driving. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62666

Chicago Manual of Style (16th Edition):

Williams, Grady Robert. “Model predictive path integral control: Theoretical foundations and applications to autonomous driving.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/62666.

MLA Handbook (7th Edition):

Williams, Grady Robert. “Model predictive path integral control: Theoretical foundations and applications to autonomous driving.” 2019. Web. 05 Mar 2021.

Vancouver:

Williams GR. Model predictive path integral control: Theoretical foundations and applications to autonomous driving. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/62666.

Council of Science Editors:

Williams GR. Model predictive path integral control: Theoretical foundations and applications to autonomous driving. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/62666


Georgia Tech

4. Cheng, Ching An. Efficient and principled robot learning: Theory and algorithms.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Roboticists have long envisioned fully-automated robots that can operate reliably in unstructured environments. This is an exciting but extremely difficult problem; in order to succeed,… (more)

Subjects/Keywords: Online learning; Control theory; Robotics; Optimization; Reinforcement learning; Imitation learning

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APA (6th Edition):

Cheng, C. A. (2020). Efficient and principled robot learning: Theory and algorithms. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62733

Chicago Manual of Style (16th Edition):

Cheng, Ching An. “Efficient and principled robot learning: Theory and algorithms.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/62733.

MLA Handbook (7th Edition):

Cheng, Ching An. “Efficient and principled robot learning: Theory and algorithms.” 2020. Web. 05 Mar 2021.

Vancouver:

Cheng CA. Efficient and principled robot learning: Theory and algorithms. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/62733.

Council of Science Editors:

Cheng CA. Efficient and principled robot learning: Theory and algorithms. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62733


Georgia Tech

5. Schroecker, Yannick Karl Daniel. Manipulating state space distributions for sample-efficient imitation-learning.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Imitation learning has emerged as one of the most effective approaches to train agents to act intelligently in unstructured and unknown domains. On its own… (more)

Subjects/Keywords: Imitation learning; Reinforcement learning; Deep learning; Machine learning; Artificial intelligence

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APA (6th Edition):

Schroecker, Y. K. D. (2020). Manipulating state space distributions for sample-efficient imitation-learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62755

Chicago Manual of Style (16th Edition):

Schroecker, Yannick Karl Daniel. “Manipulating state space distributions for sample-efficient imitation-learning.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/62755.

MLA Handbook (7th Edition):

Schroecker, Yannick Karl Daniel. “Manipulating state space distributions for sample-efficient imitation-learning.” 2020. Web. 05 Mar 2021.

Vancouver:

Schroecker YKD. Manipulating state space distributions for sample-efficient imitation-learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/62755.

Council of Science Editors:

Schroecker YKD. Manipulating state space distributions for sample-efficient imitation-learning. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62755


Georgia Tech

6. You, Changxi. Autonomous aggressive driving: theory & experiments.

Degree: PhD, Department, 2020, Georgia Tech

 Autonomous vehicles represent a major trend in future intelligent transportation systems. In order to develop autonomous vehicles, this dissertation intends to understand expert driving maneuvers… (more)

Subjects/Keywords: Autonomous vehicle path; Path planning; System identification; Decision making; Overtaking; Rally racing

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APA (6th Edition):

You, C. (2020). Autonomous aggressive driving: theory & experiments. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/62872

Chicago Manual of Style (16th Edition):

You, Changxi. “Autonomous aggressive driving: theory & experiments.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/62872.

MLA Handbook (7th Edition):

You, Changxi. “Autonomous aggressive driving: theory & experiments.” 2020. Web. 05 Mar 2021.

Vancouver:

You C. Autonomous aggressive driving: theory & experiments. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/62872.

Council of Science Editors:

You C. Autonomous aggressive driving: theory & experiments. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/62872


Georgia Tech

7. Shaban, Amirreza. Low-shot learning for object recognition, detection, and segmentation.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Deep Neural Networks are powerful at solving classification problems in computer vision. However, learning classifiers with these models requires a large amount of labeled training… (more)

Subjects/Keywords: Few-shot learning; Low-shot learning; Bi-level optimization; Few-shot semantic segmentation; Video object segmentation; Weakly-supervised few-shot object detection

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APA (6th Edition):

Shaban, A. (2020). Low-shot learning for object recognition, detection, and segmentation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63599

Chicago Manual of Style (16th Edition):

Shaban, Amirreza. “Low-shot learning for object recognition, detection, and segmentation.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/63599.

MLA Handbook (7th Edition):

Shaban, Amirreza. “Low-shot learning for object recognition, detection, and segmentation.” 2020. Web. 05 Mar 2021.

Vancouver:

Shaban A. Low-shot learning for object recognition, detection, and segmentation. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/63599.

Council of Science Editors:

Shaban A. Low-shot learning for object recognition, detection, and segmentation. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63599


Georgia Tech

8. Yan, Xinyan. Efficient trajectory and policy optimization using dynamics models.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Data-driven approaches hold the promise of creating the next wave of robots that can perform diverse tasks and adapt to unstructured environments. However, gathering data… (more)

Subjects/Keywords: Trajectory optimization; Policy optimization; State estimation; Model predictive control; Online learning; Statistical learning

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APA (6th Edition):

Yan, X. (2020). Efficient trajectory and policy optimization using dynamics models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63621

Chicago Manual of Style (16th Edition):

Yan, Xinyan. “Efficient trajectory and policy optimization using dynamics models.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/63621.

MLA Handbook (7th Edition):

Yan, Xinyan. “Efficient trajectory and policy optimization using dynamics models.” 2020. Web. 05 Mar 2021.

Vancouver:

Yan X. Efficient trajectory and policy optimization using dynamics models. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/63621.

Council of Science Editors:

Yan X. Efficient trajectory and policy optimization using dynamics models. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63621


Georgia Tech

9. Park, Daehyung. A multimodal execution monitor for assistive robots.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 Assistive robots have the potential to serve as caregivers, providing assistance with activities of daily living to people with disabilities. Monitoring when something has gone… (more)

Subjects/Keywords: Execution monitor; Assistive robot

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APA (6th Edition):

Park, D. (2018). A multimodal execution monitor for assistive robots. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59860

Chicago Manual of Style (16th Edition):

Park, Daehyung. “A multimodal execution monitor for assistive robots.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/59860.

MLA Handbook (7th Edition):

Park, Daehyung. “A multimodal execution monitor for assistive robots.” 2018. Web. 05 Mar 2021.

Vancouver:

Park D. A multimodal execution monitor for assistive robots. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/59860.

Council of Science Editors:

Park D. A multimodal execution monitor for assistive robots. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/59860


Georgia Tech

10. Hrolenok, Brian Paul. Constructing and evaluating executable models of collective behavior.

Degree: PhD, Computer Science, 2018, Georgia Tech

 Multiagent simulation (MAS) can be a valuable tool for biologists and ethologists studying collective animal behavior. However, constructing models for simulation is often a time-consuming… (more)

Subjects/Keywords: Executable models; Machine learning; Multiagent systems

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APA (6th Edition):

Hrolenok, B. P. (2018). Constructing and evaluating executable models of collective behavior. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60751

Chicago Manual of Style (16th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/60751.

MLA Handbook (7th Edition):

Hrolenok, Brian Paul. “Constructing and evaluating executable models of collective behavior.” 2018. Web. 05 Mar 2021.

Vancouver:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/60751.

Council of Science Editors:

Hrolenok BP. Constructing and evaluating executable models of collective behavior. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60751


Georgia Tech

11. Zafar, Munzir. Whole body control of wheeled inverted pendulum humanoids.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

 A framework for controlling a Wheeled Inverted Pendulum (WIP) Humanoid to perform useful interactions with the environment, while dynamically balancing itself on two wheels, was… (more)

Subjects/Keywords: Whole body control; Wheeled inverted pendulum; Humanoids; Hierarchical; Optimization; Operational space; Model predictive control

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APA (6th Edition):

Zafar, M. (2019). Whole body control of wheeled inverted pendulum humanoids. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61739

Chicago Manual of Style (16th Edition):

Zafar, Munzir. “Whole body control of wheeled inverted pendulum humanoids.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/61739.

MLA Handbook (7th Edition):

Zafar, Munzir. “Whole body control of wheeled inverted pendulum humanoids.” 2019. Web. 05 Mar 2021.

Vancouver:

Zafar M. Whole body control of wheeled inverted pendulum humanoids. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/61739.

Council of Science Editors:

Zafar M. Whole body control of wheeled inverted pendulum humanoids. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61739

12. Sawhney, Rahul. Robust approaches and optimization for 3D data.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 We introduce a robust, purely geometric, representation framework for fundamental association and analysis problems involving multiple views and scenes. The framework utilizes surface patches /… (more)

Subjects/Keywords: 3D; Geometry; Robust; Optimization; Association; Retrieval; Robust loss; Viewpoint invariance; RGB-D; Depth image; Point cloud; Geometric description; Matching; Correspondence; Registration; Reconstruction; Surface; Patch; Segment; Superpixel; Majorization minorization; Outlier rejection; Model fitting; Estimation; Nonlinear least absolute deviations; Mean shift; Mode seeking; Segmentation; Hierarchical; Geometric diversity; Nonsmooth; Nonconvex; Edit distance; Damerau Levenshtein; Proximal algorithms; Determinantal point process; Fisher vector; Feature space; Variational factorization; M - estimation; Structured estimation

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APA (6th Edition):

Sawhney, R. (2018). Robust approaches and optimization for 3D data. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61103

Chicago Manual of Style (16th Edition):

Sawhney, Rahul. “Robust approaches and optimization for 3D data.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/61103.

MLA Handbook (7th Edition):

Sawhney, Rahul. “Robust approaches and optimization for 3D data.” 2018. Web. 05 Mar 2021.

Vancouver:

Sawhney R. Robust approaches and optimization for 3D data. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/61103.

Council of Science Editors:

Sawhney R. Robust approaches and optimization for 3D data. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61103


Georgia Tech

13. Drews, Paul Michael. Visual attention for high speed driving.

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

 Coupling of control and perception is an especially difficult problem. This thesis investigates this problem in the context of aggressive off-road driving. By jointly developing… (more)

Subjects/Keywords: Robotics; Computer vision; Autonomous vehicles; Neural networks; High speed

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APA (6th Edition):

Drews, P. M. (2018). Visual attention for high speed driving. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61183

Chicago Manual of Style (16th Edition):

Drews, Paul Michael. “Visual attention for high speed driving.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/61183.

MLA Handbook (7th Edition):

Drews, Paul Michael. “Visual attention for high speed driving.” 2018. Web. 05 Mar 2021.

Vancouver:

Drews PM. Visual attention for high speed driving. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/61183.

Council of Science Editors:

Drews PM. Visual attention for high speed driving. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/61183


Georgia Tech

14. Vijayakumar, Ashwin Kalyan. Improved search techniques for structured prediction.

Degree: PhD, Interactive Computing, 2020, Georgia Tech

 Many useful AI tasks like machine translation, captioning or program syn- thesis to name a few can be abstracted as structured prediction problems. For these… (more)

Subjects/Keywords: Sequence decoding; Program synthesis

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APA (6th Edition):

Vijayakumar, A. K. (2020). Improved search techniques for structured prediction. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/63701

Chicago Manual of Style (16th Edition):

Vijayakumar, Ashwin Kalyan. “Improved search techniques for structured prediction.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/63701.

MLA Handbook (7th Edition):

Vijayakumar, Ashwin Kalyan. “Improved search techniques for structured prediction.” 2020. Web. 05 Mar 2021.

Vancouver:

Vijayakumar AK. Improved search techniques for structured prediction. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/63701.

Council of Science Editors:

Vijayakumar AK. Improved search techniques for structured prediction. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/63701


Georgia Tech

15. Rana, Muhammad Asif. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.

Degree: PhD, Electrical and Computer Engineering, 2020, Georgia Tech

 Functioning in the real world requires robots to reason about and generate motions for execution of complex tasks, in potentially unstructured and dynamic environments. Early… (more)

Subjects/Keywords: learning from demonstration; robot learning

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APA (6th Edition):

Rana, M. A. (2020). Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/64106

Chicago Manual of Style (16th Edition):

Rana, Muhammad Asif. “Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.” 2020. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/64106.

MLA Handbook (7th Edition):

Rana, Muhammad Asif. “Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics.” 2020. Web. 05 Mar 2021.

Vancouver:

Rana MA. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. [Internet] [Doctoral dissertation]. Georgia Tech; 2020. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/64106.

Council of Science Editors:

Rana MA. Methods for Teaching Diverse Robot Skills: Leveraging Priors, Geometry, and Dynamics. [Doctoral Dissertation]. Georgia Tech; 2020. Available from: http://hdl.handle.net/1853/64106

16. Hauer, Florian M. Path-planning algorithms in high-dimensional spaces.

Degree: PhD, Aerospace Engineering, 2019, Georgia Tech

 In this thesis, we discuss the problem of path-planning in high-dimensional spaces. Large search spaces tend to lead to slow algorithms in order to find… (more)

Subjects/Keywords: Path-planning; RRT; Sampling-based; Algorithms; Completeness; Graph; Search

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APA (6th Edition):

Hauer, F. M. (2019). Path-planning algorithms in high-dimensional spaces. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61206

Chicago Manual of Style (16th Edition):

Hauer, Florian M. “Path-planning algorithms in high-dimensional spaces.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/61206.

MLA Handbook (7th Edition):

Hauer, Florian M. “Path-planning algorithms in high-dimensional spaces.” 2019. Web. 05 Mar 2021.

Vancouver:

Hauer FM. Path-planning algorithms in high-dimensional spaces. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/61206.

Council of Science Editors:

Hauer FM. Path-planning algorithms in high-dimensional spaces. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61206

17. Dong, Jing. Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture.

Degree: PhD, Interactive Computing, 2018, Georgia Tech

 Crop monitoring is one of the most important tasks in precision agriculture, and to reduce cost, such task is often performed autonomously by unmanned aerial… (more)

Subjects/Keywords: Precision agriculture; SLAM; Spatio-temporal reconstruction; Weakly-supervised learning

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APA (6th Edition):

Dong, J. (2018). Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/60742

Chicago Manual of Style (16th Edition):

Dong, Jing. “Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture.” 2018. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/60742.

MLA Handbook (7th Edition):

Dong, Jing. “Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture.” 2018. Web. 05 Mar 2021.

Vancouver:

Dong J. Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture. [Internet] [Doctoral dissertation]. Georgia Tech; 2018. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/60742.

Council of Science Editors:

Dong J. Extending 3D reconstruction to temporal and multi-model sensor data for precision agriculture. [Doctoral Dissertation]. Georgia Tech; 2018. Available from: http://hdl.handle.net/1853/60742

18. Ji, Yangfeng. Semantic representation learning for discourse processing.

Degree: PhD, Computer Science, 2016, Georgia Tech

 Discourse processing is to identify coherent relations, such as contrast and causal relation, from well-organized texts. The outcomes from discourse processing can benefit both research… (more)

Subjects/Keywords: Semantics; Representation learning; Deep learning; Discourse; Discourse processing; Sentiment analysis

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APA (6th Edition):

Ji, Y. (2016). Semantic representation learning for discourse processing. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55636

Chicago Manual of Style (16th Edition):

Ji, Yangfeng. “Semantic representation learning for discourse processing.” 2016. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/55636.

MLA Handbook (7th Edition):

Ji, Yangfeng. “Semantic representation learning for discourse processing.” 2016. Web. 05 Mar 2021.

Vancouver:

Ji Y. Semantic representation learning for discourse processing. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/55636.

Council of Science Editors:

Ji Y. Semantic representation learning for discourse processing. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55636

19. Mukadam, Mustafa. Structured learning and inference for robot motion generation.

Degree: PhD, Electrical and Computer Engineering, 2019, Georgia Tech

 The ability to generate motions that accomplish desired tasks is fundamental to any robotic system. Robots must be able to generate such motions in a… (more)

Subjects/Keywords: Motion planning; Machine learning

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APA (6th Edition):

Mukadam, M. (2019). Structured learning and inference for robot motion generation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/61714

Chicago Manual of Style (16th Edition):

Mukadam, Mustafa. “Structured learning and inference for robot motion generation.” 2019. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/61714.

MLA Handbook (7th Edition):

Mukadam, Mustafa. “Structured learning and inference for robot motion generation.” 2019. Web. 05 Mar 2021.

Vancouver:

Mukadam M. Structured learning and inference for robot motion generation. [Internet] [Doctoral dissertation]. Georgia Tech; 2019. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/61714.

Council of Science Editors:

Mukadam M. Structured learning and inference for robot motion generation. [Doctoral Dissertation]. Georgia Tech; 2019. Available from: http://hdl.handle.net/1853/61714

20. Xie, Bo. Algorithms and analysis for non-convex optimization problems in machine learning.

Degree: PhD, Computational Science and Engineering, 2017, Georgia Tech

 In this thesis, we propose efficient algorithms and provide theoretical analysis through the angle of spectral methods for some important non-convex optimization problems in machine… (more)

Subjects/Keywords: Machine learning; Non-convex optimization; Spectral algorithms; Neural networks; Deep learning

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

APA (6th Edition):

Xie, B. (2017). Algorithms and analysis for non-convex optimization problems in machine learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58642

Chicago Manual of Style (16th Edition):

Xie, Bo. “Algorithms and analysis for non-convex optimization problems in machine learning.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/58642.

MLA Handbook (7th Edition):

Xie, Bo. “Algorithms and analysis for non-convex optimization problems in machine learning.” 2017. Web. 05 Mar 2021.

Vancouver:

Xie B. Algorithms and analysis for non-convex optimization problems in machine learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/58642.

Council of Science Editors:

Xie B. Algorithms and analysis for non-convex optimization problems in machine learning. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58642

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

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 March 05, 2021. http://hdl.handle.net/1853/53592.

MLA Handbook (7th Edition):

Charles, Adam Shabti. “Dynamics and correlations in sparse signal acquisition.” 2015. Web. 05 Mar 2021.

Vancouver:

Charles AS. Dynamics and correlations in sparse signal acquisition. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2021 Mar 05]. 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

22. Yang, Yi. Robust adaptation of natural language processing for language variation.

Degree: PhD, Computer Science, 2017, Georgia Tech

 Natural language processing (NLP) technology has been applied in various domains, ranging from social media and digital humanities to public health. Unfortunately, the adoption of… (more)

Subjects/Keywords: Natural language processing; Machine learning

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

APA (6th Edition):

Yang, Y. (2017). Robust adaptation of natural language processing for language variation. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/58201

Chicago Manual of Style (16th Edition):

Yang, Yi. “Robust adaptation of natural language processing for language variation.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/58201.

MLA Handbook (7th Edition):

Yang, Yi. “Robust adaptation of natural language processing for language variation.” 2017. Web. 05 Mar 2021.

Vancouver:

Yang Y. Robust adaptation of natural language processing for language variation. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/58201.

Council of Science Editors:

Yang Y. Robust adaptation of natural language processing for language variation. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/58201

23. Pan, Yunpeng. Learning control via probabilistic trajectory optimization.

Degree: PhD, Aerospace Engineering, 2017, Georgia Tech

 A central problem in the field of robotics is to develop real-time planning and control algorithms for autonomous systems to behave intelligently under uncertainty. While… (more)

Subjects/Keywords: Optimal control; Robotics; Artificial intelligence; Machine learning

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

APA (6th Edition):

Pan, Y. (2017). Learning control via probabilistic trajectory optimization. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/59278

Chicago Manual of Style (16th Edition):

Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Doctoral Dissertation, Georgia Tech. Accessed March 05, 2021. http://hdl.handle.net/1853/59278.

MLA Handbook (7th Edition):

Pan, Yunpeng. “Learning control via probabilistic trajectory optimization.” 2017. Web. 05 Mar 2021.

Vancouver:

Pan Y. Learning control via probabilistic trajectory optimization. [Internet] [Doctoral dissertation]. Georgia Tech; 2017. [cited 2021 Mar 05]. Available from: http://hdl.handle.net/1853/59278.

Council of Science Editors:

Pan Y. Learning control via probabilistic trajectory optimization. [Doctoral Dissertation]. Georgia Tech; 2017. Available from: http://hdl.handle.net/1853/59278

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