Index Catalog // ScholarsArchive@OSU

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

Creator:: Senewiratne, Yomal K.
Abstract:: We present a method for decentralized, multi-robot exploration in adverse environments where communication is minimal. A key conceptual feature of our method is enabling implicit coordination between robots by training a Convolutional Neural Network (CNN) as a heuristic for planning using Monte Carlo Tree Search (MCTS). Our method consists of...
Resource Type:: Masters Thesis

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Creator:: Khorram, Saeed
Abstract:: In this thesis, we introduce a novel Explanation Neural Network (XNN) to explain the predictions made by a deep network. The XNN works by embedding a high-dimensional activation vector of a deep network layer non-linearly into a low-dimensional explanation space while retaining faithfulness i.e., the original deep learning predictions can...
Resource Type:: Masters Thesis

Interactive player tracking for videos in American football

Creator:: Bawaskar, Amit
Abstract:: This thesis presents an interactive software tool for tracking a moving object in a video. In particular, we focus on the problem of tracking a player in American football videos. Object tracking is one of the fundamental problems in computer vision. It is one of the most important components in...
Resource Type:: Masters Thesis

Action Segmentation with Limited Supervision

Creator:: Li, Jun
Abstract:: In this dissertation, we address action segmentation in videos under limited supervision. The goal of action segmentation is to predict an action class for each frame of a video. The limited supervision means ground truth labels of video frames are not available in training. We focus on three types of...
Resource Type:: Dissertation

Fine-Grained Object Recognition Under Limited Training Data

Creator:: Lam, Michael Q.
Abstract:: This dissertation addresses object recognition in challenging settings, where distinct object classes are visually very similar (e.g., species of birds and insects) and/or access to training examples of object classes is limited (e.g., due to the associated high costs of data annotation). In this dissertation, we present a variety of...
Resource Type:: Dissertation

Data-Driven Approaches For Decoding Volitional Movement Intent From Bioelectrical Signals

Creator:: Cunha Dantas, Henrique
Abstract:: There are nearly two million limb amputees living in the United States of America. Loss of limbs results in profound changes in one's life. However, the underlying neural circuitry and much of the ability to sense and control movements of their missing limb is retained even after limb loss. This...
Resource Type:: Dissertation
Full Text:: various perspectives. I also want to thank Dr. David Warren and Dr. Gregory Clark for the collaborative

Activity recognition in desktop environments

Creator:: Shen, Jianqiang
Abstract:: Knowledge workers are struggling in the information flood. There is a growing interest in intelligent desktop environments that help knowledge workers organize their daily life. Intelligent desktop environments allow the desktop user to define a set of “activities” that characterize the user’s desktop work. These environments then attempt to identify...
Resource Type:: Dissertation

Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

Creator:: Khorram, Saeed
Abstract:: The advancement of artificial intelligence (AI) has led to transformative developments across multiple sectors, fostering innovation and redefining our interactions with technology. As AI matures and becomes integrated into society, it offers numerous opportunities to address global challenges and revolutionize a wide array of human endeavors. These advances are driven...
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

Characterizing and Addressing Dataset Shift with Deep Learning

Creator:: Olson, Matthew L.
Abstract:: This dissertation delves into understanding, characterizing, and addressing dataset shift in deep learning, a pervasive issue for deployed machine learning systems. Integral aspects of the problem are examined: We start with the use of counterfactual explanations in order to characterize the behavior of deep reinforcement learning agents in visual input...
Resource Type:: Dissertation

ScholarsArchive@OSU

Decentralized Multi-Robot Exploration with Sparse Communication through Deep Imitation Learning

Toward Disentangling the Activations of the Deep Networks via Low-dimensional Embedding and Non-negative Factorization

Interactive player tracking for videos in American football

Action Segmentation with Limited Supervision

Fine-Grained Object Recognition Under Limited Training Data

Data-Driven Approaches For Decoding Volitional Movement Intent From Bioelectrical Signals

Activity recognition in desktop environments

Generative Models Meet Explainable AI and Long-Tail Learning: from Attribution Maps to Image Synthesis

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

Characterizing and Addressing Dataset Shift with Deep Learning

Limit your search

Academic Affiliation

Advisor

Commencement Year

Committee Member

Creator

Contributor

Date

Decade

Degree Field

Degree Level

Degree Name

Language

License

Non-Academic Affiliation

Peer Reviewed

Resource Type

Rights Statement

Subject

Search Constraints

Search Results

Limit your search