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You searched for +publisher:"University of Texas – Austin" +contributor:("Niekum, Scott"). Showing records 1 – 8 of 8 total matches.

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University of Texas – Austin

1. -4766-7868. Self-learning control of automated drilling operations.

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

APA (6th Edition):

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

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):

-4766-7868. “Self-learning control of automated drilling operations.” 2018. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/65829.

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):

-4766-7868. “Self-learning control of automated drilling operations.” 2018. Web. 16 Feb 2019.

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] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Feb 16]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-4766-7868. Self-learning control of automated drilling operations. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

2. -9199-0633. Continually improving grounded natural language understanding through human-robot dialog.

Degree: Computer Sciences, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-9199-0633. (2018). Continually improving grounded natural language understanding through human-robot dialog. (Thesis). 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
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/68120.

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):

-9199-0633. “Continually improving grounded natural language understanding through human-robot dialog.” 2018. Web. 16 Feb 2019.

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] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Feb 16]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-9199-0633. Continually improving grounded natural language understanding through human-robot dialog. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation

3. Chiang, Kai-Yang. Statistical analysis for modeling dyadic interactions using machine learning methods.

Degree: Computer Sciences, 2017, University of Texas – Austin

 Modeling dyadic interactions between entities is one of the fundamental problems in machine learning with many real-world applications, including recommender systems, data clustering, social network… (more)

Subjects/Keywords: Dyadic interaction modeling; Statistical machine learning; Signed network analysis; Signed graph clustering; Dyadic rank aggregation; Matrix completion; Robust PCA; Side information

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

APA (6th Edition):

Chiang, K. (2017). Statistical analysis for modeling dyadic interactions using machine learning methods. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/47368

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):

Chiang, Kai-Yang. “Statistical analysis for modeling dyadic interactions using machine learning methods.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/47368.

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

MLA Handbook (7th Edition):

Chiang, Kai-Yang. “Statistical analysis for modeling dyadic interactions using machine learning methods.” 2017. Web. 16 Feb 2019.

Vancouver:

Chiang K. Statistical analysis for modeling dyadic interactions using machine learning methods. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16]. Available from: http://hdl.handle.net/2152/47368.

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

Council of Science Editors:

Chiang K. Statistical analysis for modeling dyadic interactions using machine learning methods. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/47368

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


University of Texas – Austin

4. -6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents.

Degree: Computer Sciences, 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 · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

-6763-2625. (2017). Multilayered skill learning and movement coordination for autonomous robotic agents. (Thesis). 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
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/62889.

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):

-6763-2625. “Multilayered skill learning and movement coordination for autonomous robotic agents.” 2017. Web. 16 Feb 2019.

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] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-6763-2625. Multilayered skill learning and movement coordination for autonomous robotic agents. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation


University of Texas – Austin

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

Degree: Computer Sciences, 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. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/63453

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):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/63453.

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

MLA Handbook (7th Edition):

Jain, Suyog Dutt. “Human machine collaboration for foreground segmentation in images and videos.” 2018. Web. 16 Feb 2019.

Vancouver:

Jain SD. Human machine collaboration for foreground segmentation in images and videos. [Internet] [Thesis]. University of Texas – Austin; 2018. [cited 2019 Feb 16]. Available from: http://hdl.handle.net/2152/63453.

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

Council of Science Editors:

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

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


University of Texas – Austin

6. -6888-3095. Embodied learning for visual recognition.

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

APA (6th Edition):

-6888-3095. (2017). Embodied learning for visual recognition. (Thesis). 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
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

-6888-3095. “Embodied learning for visual recognition.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/63489.

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):

-6888-3095. “Embodied learning for visual recognition.” 2017. Web. 16 Feb 2019.

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] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16]. 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
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

-6888-3095. Embodied learning for visual recognition. [Thesis]. 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
Not specified: Masters Thesis or Doctoral Dissertation

7. -8188-7093. A deep learning framework for model-free 6 degree of freedom object tracking.

Degree: Computer Sciences, 2017, University of Texas – Austin

 In this work we address the challenging task of 6 degree of freedom (DoF), model-free object tracking. We propose a new deep learning framework that… (more)

Subjects/Keywords: Object-tracking; 6 DoF; Deep learning; Model-free; Pose prediction

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

APA (6th Edition):

-8188-7093. (2017). A deep learning framework for model-free 6 degree of freedom object tracking. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/60458

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):

-8188-7093. “A deep learning framework for model-free 6 degree of freedom object tracking.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/60458.

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):

-8188-7093. “A deep learning framework for model-free 6 degree of freedom object tracking.” 2017. Web. 16 Feb 2019.

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

Vancouver:

-8188-7093. A deep learning framework for model-free 6 degree of freedom object tracking. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16]. Available from: http://hdl.handle.net/2152/60458.

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:

-8188-7093. A deep learning framework for model-free 6 degree of freedom object tracking. [Thesis]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/60458

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

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

Degree: Computer Sciences, 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. (Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/61382

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):

Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Thesis, University of Texas – Austin. Accessed February 16, 2019. http://hdl.handle.net/2152/61382.

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

MLA Handbook (7th Edition):

Khandelwal, Piyush. “On-demand coordination of multiple service robots.” 2017. Web. 16 Feb 2019.

Vancouver:

Khandelwal P. On-demand coordination of multiple service robots. [Internet] [Thesis]. University of Texas – Austin; 2017. [cited 2019 Feb 16]. Available from: http://hdl.handle.net/2152/61382.

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

Council of Science Editors:

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

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

.