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Filtering by: Creator Irish, Robert James Committee Member Tadepalli, Prasad Committee Member Li, Fuxin

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
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
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...

Resource Type:

Dissertation