Index Catalog // ScholarsArchive@OSU

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
Full Text:: collaborations and Peter Anderson and Jesse Thomason for their advising insights and generosity. I thank the

Visual object discovery and understanding

Creator:: Yuan, Jialin
Abstract:: Learning to recognize objects is a fundamental and essential step in human perception and understanding of the world. Accordingly, research of object discovery across diverse modalities plays a pivotal role in the context of computer vision. This field not only contributes significantly to enhancing our understanding of visual information but...
Resource Type:: Dissertation

Inclusive Software Design and Its Methods for Beyond-WIMP User Interfaces

Creator:: Fallatah, Abrar
Abstract:: While digital inclusivity researchers and software practitioners have been trying to address exclusion biases in Windows, Icons, Menus, and Pointers (WIMP) user interfaces (UIs) for a long time, little has been done to investigate if and how inclusive software design and its methods that have been devised for WIMP UIs...
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

Stake-Free Evaluations of Black-Box Optimization and Spatio-Temporal Graph Network Algorithms

Creator:: Merrill, Erich, III
Abstract:: Papers proposing novel machine learning algorithms tend to present the algorithm or technique in question in the best possible light. The standard practice is generally for authors to emphasize their proposed algorithms' performance in the precise setting where it is maximally impressive, often by only fully evaluating their best known...
Resource Type:: Dissertation

Deep Learning for Human and Biological Languages

Creator:: Hong, Junki
Abstract:: We explore the application of deep learning to the disparate fields of natural language processing and computational biology. Both the sentences uttered by humans as well as the RNA and protein sequences found within the cells of their bodies can be considered formal languages in computer science, as sets of...
Resource Type:: Dissertation

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

An Information Modeling Framework for Support of Sustainable Manufacturing System Design Decision Making

Creator:: Shankar Raman, Arvind
Abstract:: Manufacturing technology has continuously evolved and advanced over the past century; this has led to an increase in the production of consumer and industrial goods driven by simultaneous growth in population and wealth. Despite the resulting economic and labor growth, environmental impacts of manufacturing have increased dramatically due to the...
Resource Type:: Dissertation

Auxiliary Tasks for Efficient Learning of Point-Goal Navigation

Creator:: Desai, Saurabh Satish
Abstract:: Top-performing approaches to embodied AI tasks like point-goal navigation often rely on training agents via reinforcement learning over tens of millions (or even billions) of experiential steps – learning neural agents that map directly from visual observations to actions. In this work, we question whether these extreme training durations are...
Resource Type:: Masters Thesis

Learning and Improving Policies for Probabilistic Planning Problems

Creator:: Issakkimuthu, Murugeswari
Abstract:: In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
Resource Type:: Dissertation

ScholarsArchive@OSU

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

Visual object discovery and understanding

Inclusive Software Design and Its Methods for Beyond-WIMP User Interfaces

Investigating Latent State and Uncertainty Representations in Reinforcement Learning

Stake-Free Evaluations of Black-Box Optimization and Spatio-Temporal Graph Network Algorithms

Deep Learning for Human and Biological Languages

Explanations and Processes to Enable Humans to Assess AI with Respect to Manipulable Properties

An Information Modeling Framework for Support of Sustainable Manufacturing System Design Decision Making

Auxiliary Tasks for Efficient Learning of Point-Goal Navigation

Learning and Improving Policies for Probabilistic Planning Problems

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