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Filtering by: Creator Kuligowski, David R. Remove constraint Creator: Kuligowski, David R. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Committee Member Li, Fuxin Remove constraint Committee Member: Li, Fuxin

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Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Creator:

Koul, Anurag

Abstract:

Learning latent space representations of high-dimensional world states has been at the core of recent rapid growth in reinforcement learning(RL). At the same time, RL algo- rithms have suffered from ignored uncertainties in the predicted estimates of model-free or model-based methods. In our work, we investigate both of these aspects...

Resource Type:

Dissertation

Full Text:

David Zerbe as well as all my housemates in the “International House”. Over the years, meeting people
Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

Creator:

Dodge, Jonathan E.

Abstract:

Assessing AI systems is difficult. Humans rely on AI systems in increasing ways, both visible and invisible, meaning a variety of stakeholders need a variety of assessment tools (e.g., a professional auditor, a developer, and an end user all have different needs). We posit that it is possible to provide...

Resource Type:

Dissertation
Object Tracking-by-Segmentation in Videos

Creator:

Chen, Sheng

Abstract:

This thesis focuses on the problem of object tracking. Given a video, the general objective of tracking is to track the location over time of one or more targets in the image sequence. This is a very challenging task as algorithms need to deal with problems such as appearance variations,...

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Dissertation
Semantic embodied navigation : developing agents that navigate from language and vision

Creator:

Krantz, Jacob

Abstract:

Autonomous robotic agents are on their way to becoming in-home personal assistants, construction assistants, and warehouse workers. The degree of autonomy of such systems is reflected by the manner in which we specify goals to them; the abstraction of low-level commands to high-level goals goes hand-in-hand with increased autonomy. In...

Resource Type:

Dissertation
Action Segmentation in Videos: Deep Models, Prediction Diffusion, and Deep-Temporal Augmentations

Creator:

Aziere, Nicolas

Abstract:

This paper addresses the high model complexity and overconfident frame labeling of state-of-the-art (SOTA) action segmenters. Their complexity is typically justified by the need to sequentially refine action segmentation through multiple stages of a deep architecture. However, this multistage refinement does not take into account uncertainty of frame labeling predicted...

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Dissertation

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Academic Affiliation

Electrical Engineering and Computer Science5

Advisor

Fern, Alan2
Burnett, Margaret M.1
Dietterich, Thomas G.1
Lee, Stefan1
Todorovic, Sinisa1

Commencement Year

Commencement Year range begin – Commencement Year range end

Current results range from 2017 to 2024

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Committee Member

Li, Fuxin[remove]5
Tadepalli, Prasad[remove]5
Bae, Harold1
Fern, Alan1
Fern, Xiaoli1
more Committee Members »

Creator

Aziere, Nicolas1
Chen, Sheng1
Dodge, Jonathan E.1
Koul, Anurag1
Krantz, Jacob1

Date

Date range begin – Date range end

Current results range from 2017 to 2024

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Decade

2020-20294
2010-20191

Degree Field

Computer Science5

Degree Level

Doctoral5

Degree Name

Doctor of Philosophy (Ph.D.)5

File Format

pdf1

Language

English [eng]5

License

All rights reserved5

Non-Academic Affiliation

Oregon State University. Graduate School1

Peer Reviewed

No5

Resource Type

Dissertation5

Rights Statement

In Copyright5

Subject

Image processing1
Image segmentation1
Neural networks (Computer science)1
Video recordings1

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