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You searched for +publisher:"University of Texas – Austin" +contributor:("Niekum, Scott"). Showing records 1 – 11 of 11 total matches.

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University of Texas – Austin

1. -7411-0398. Data efficient reinforcement learning with off-policy and simulated data.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Learning from interaction with the environment  – trying untested actions, observing successes and failures, and tying effects back to causes  – is one of the… (more)

Subjects/Keywords: Artificial intelligence; Reinforcement learning; Robotics; Off-policy; Sim-to-real

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APA (6th Edition):

-7411-0398. (2019). Data efficient reinforcement learning with off-policy and simulated data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/7716

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

Chicago Manual of Style (16th Edition):

-7411-0398. “Data efficient reinforcement learning with off-policy and simulated data.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://dx.doi.org/10.26153/tsw/7716.

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

MLA Handbook (7th Edition):

-7411-0398. “Data efficient reinforcement learning with off-policy and simulated data.” 2019. Web. 27 Sep 2020.

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

Vancouver:

-7411-0398. Data efficient reinforcement learning with off-policy and simulated data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Sep 27]. Available from: http://dx.doi.org/10.26153/tsw/7716.

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

Council of Science Editors:

-7411-0398. Data efficient reinforcement learning with off-policy and simulated data. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/7716

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


University of Texas – Austin

2. Xiong, Bo. Learning to compose photos and videos from passive cameras.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Photo and video overload is well-known to most computer users. With cameras on mobile devices, it is all too easy to snap images and videos… (more)

Subjects/Keywords: Passive cameras; Video highlight detection; Snap point detection; Image segmentation; Video segmentation; Viewing panoramas

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APA (6th Edition):

Xiong, B. (2019). Learning to compose photos and videos from passive cameras. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/5847

Chicago Manual of Style (16th Edition):

Xiong, Bo. “Learning to compose photos and videos from passive cameras.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://dx.doi.org/10.26153/tsw/5847.

MLA Handbook (7th Edition):

Xiong, Bo. “Learning to compose photos and videos from passive cameras.” 2019. Web. 27 Sep 2020.

Vancouver:

Xiong B. Learning to compose photos and videos from passive cameras. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Sep 27]. Available from: http://dx.doi.org/10.26153/tsw/5847.

Council of Science Editors:

Xiong B. Learning to compose photos and videos from passive cameras. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/5847


University of Texas – Austin

3. -2711-6738. Learning for 360° video compression, recognition, and display.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 360° cameras are a core building block of the Virtual Reality (VR) and Augmented Reality (AR) technology that bridges the real and digital worlds. It… (more)

Subjects/Keywords: 360° video; Omnidirectional media; Video analysis

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APA (6th Edition):

-2711-6738. (2019). Learning for 360° video compression, recognition, and display. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://dx.doi.org/10.26153/tsw/5848

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

Chicago Manual of Style (16th Edition):

-2711-6738. “Learning for 360° video compression, recognition, and display.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://dx.doi.org/10.26153/tsw/5848.

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

MLA Handbook (7th Edition):

-2711-6738. “Learning for 360° video compression, recognition, and display.” 2019. Web. 27 Sep 2020.

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

Vancouver:

-2711-6738. Learning for 360° video compression, recognition, and display. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Sep 27]. Available from: http://dx.doi.org/10.26153/tsw/5848.

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

Council of Science Editors:

-2711-6738. Learning for 360° video compression, recognition, and display. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://dx.doi.org/10.26153/tsw/5848

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

4. Rawal, Aditya, Ph. D. in computer science. Discovering gated recurrent neural network architectures.

Degree: PhD, Computer science, 2019, University of Texas – Austin

 Reinforcement Learning agent networks with memory are a key component in solving POMDP tasks. Gated recurrent networks such as those composed of Long Short-Term Memory… (more)

Subjects/Keywords: Recurrent neural networks; Neuroevolution; Network architecture search; Meta-learning; Reinforcement learning; Language modeling; Music modeling

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APA (6th Edition):

Rawal, Aditya, P. D. i. c. s. (2019). Discovering gated recurrent neural network architectures. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/72839

Chicago Manual of Style (16th Edition):

Rawal, Aditya, Ph D in computer science. “Discovering gated recurrent neural network architectures.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/72839.

MLA Handbook (7th Edition):

Rawal, Aditya, Ph D in computer science. “Discovering gated recurrent neural network architectures.” 2019. Web. 27 Sep 2020.

Vancouver:

Rawal, Aditya PDics. Discovering gated recurrent neural network architectures. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/72839.

Council of Science Editors:

Rawal, Aditya PDics. Discovering gated recurrent neural network architectures. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://hdl.handle.net/2152/72839

5. Mahjourian, Reza. Hierarchical policy design for sample-efficient learning of robot table tennis through self-play.

Degree: PhD, Computer Science, 2019, University of Texas – Austin

 Training robots with physical bodies requires developing new methods and action representations that allow the learning agents to explore the space of policies efficiently. This… (more)

Subjects/Keywords: Robotics; Table tennis; Self-play; Reinforcement learning; Hierarchical policy

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APA (6th Edition):

Mahjourian, R. (2019). Hierarchical policy design for sample-efficient learning of robot table tennis through self-play. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/72812

Chicago Manual of Style (16th Edition):

Mahjourian, Reza. “Hierarchical policy design for sample-efficient learning of robot table tennis through self-play.” 2019. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/72812.

MLA Handbook (7th Edition):

Mahjourian, Reza. “Hierarchical policy design for sample-efficient learning of robot table tennis through self-play.” 2019. Web. 27 Sep 2020.

Vancouver:

Mahjourian R. Hierarchical policy design for sample-efficient learning of robot table tennis through self-play. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2019. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/72812.

Council of Science Editors:

Mahjourian R. Hierarchical policy design for sample-efficient learning of robot table tennis through self-play. [Doctoral Dissertation]. University of Texas – Austin; 2019. Available from: http://hdl.handle.net/2152/72812


University of Texas – Austin

6. -4766-7868. Self-learning control of automated drilling operations.

Degree: PhD, Mechanical Engineering, 2018, University of Texas – Austin

 In recent years, drilling automation has sparked significant interest in both the upstream oil and gas industry and the drilling research community. Automation of various… (more)

Subjects/Keywords: Automated drilling; Drilling optimization; Self-learning control

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APA (6th Edition):

-4766-7868. (2018). Self-learning control of automated drilling operations. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/65829

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Author name may be incomplete

Chicago Manual of Style (16th Edition):

-4766-7868. “Self-learning control of automated drilling operations.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/65829.

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

MLA Handbook (7th Edition):

-4766-7868. “Self-learning control of automated drilling operations.” 2018. Web. 27 Sep 2020.

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

Vancouver:

-4766-7868. Self-learning control of automated drilling operations. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/65829.

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

Council of Science Editors:

-4766-7868. Self-learning control of automated drilling operations. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/65829

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

7. -9199-0633. Continually improving grounded natural language understanding through human-robot dialog.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 As robots become ubiquitous in homes and workplaces such as hospitals and factories, they must be able to communicate with humans. Several kinds of knowledge… (more)

Subjects/Keywords: Natural language processing; Human-robot dialog

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APA (6th Edition):

-9199-0633. (2018). Continually improving grounded natural language understanding through human-robot dialog. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/68120

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

Chicago Manual of Style (16th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/68120.

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

MLA Handbook (7th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Web. 27 Sep 2020.

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

Vancouver:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/68120.

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

Council of Science Editors:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/68120

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

8. Jain, Suyog Dutt. Human machine collaboration for foreground segmentation in images and videos.

Degree: PhD, Computer Science, 2018, University of Texas – Austin

 Foreground segmentation is defined as the problem of generating pixel level foreground masks for all the objects in a given image or video. Accurate foreground… (more)

Subjects/Keywords: Computer vision; Crowdsourcing; Human machine collaboration; Image and video segmentation; Image segmentation; Video segmentation; Foreground segmentation; Object segmentation

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

APA (6th Edition):

Jain, S. D. (2018). Human machine collaboration for foreground segmentation in images and videos. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63453

Chicago Manual of Style (16th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/63453.

MLA Handbook (7th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Web. 27 Sep 2020.

Vancouver:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2018. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/63453.

Council of Science Editors:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Doctoral Dissertation]. University of Texas – Austin; 2018. Available from: http://hdl.handle.net/2152/63453


University of Texas – Austin

9. -6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents.

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

 With advances in technology expanding the capabilities of robots, while at the same time making robots cheaper to manufacture, robots are rapidly becoming more prevalent… (more)

Subjects/Keywords: Overlapping layered learning; Role assignment; Reinforcement learning; Robotics; Robot soccer

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APA (6th Edition):

-6763-2625. (2017). Multilayered skill learning and movement coordination for autonomous robotic agents. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62889

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

Chicago Manual of Style (16th Edition):

-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/62889.

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

MLA Handbook (7th Edition):

-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Web. 27 Sep 2020.

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

Vancouver:

-6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/62889.

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

Council of Science Editors:

-6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62889

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

10. -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

Note: this citation may be lacking information needed for this citation format:
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 September 27, 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. 27 Sep 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 Sep 27]. 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

11. Khandelwal, Piyush. On-demand coordination of multiple service robots.

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

 Research in recent years has made it increasingly plausible to deploy a large number of service robots in home and office environments. Given that multiple… (more)

Subjects/Keywords: Multi-robot coordination; Monte Carlo tree search; Markov decision processes; Probabilistic planning; Multi-robot systems

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

APA (6th Edition):

Khandelwal, P. (2017). On-demand coordination of multiple service robots. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61382

Chicago Manual of Style (16th Edition):

Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed September 27, 2020. http://hdl.handle.net/2152/61382.

MLA Handbook (7th Edition):

Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Web. 27 Sep 2020.

Vancouver:

Khandelwal P. On-demand coordination of multiple service robots. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Sep 27]. Available from: http://hdl.handle.net/2152/61382.

Council of Science Editors:

Khandelwal P. On-demand coordination of multiple service robots. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/61382

.