Index Catalog // ScholarsArchive@OSU

Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

Creator:: George, Damon A.
Abstract:: Movement intent decoders, which interpret volitional movement intent from human bioelectric signals, can be incorporated into modern neuroprostheses to offer people living with limb loss or paralysis the potential to regain their lost motor control. Machine learning methods have become the research standard for continuous decoders with high degrees of...
Resource Type:: Masters Thesis
Full Text:: AN ABSTRACT OF THE THESIS OF Damon A. George for the degree of

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

Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

Creator:: Nguyen, Khoi D.
Abstract:: This dissertation addresses few-shot object segmentation in images. The goal of segmentation is to label every image pixel with a class of the object occupying that pixel, where the class may represent a semantic object category or instance. In few-shot segmentation, training and test datasets have different classes. Every new...
Resource Type:: Dissertation

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

Robust and Efficient Classification of Videos in the Wild

Creator:: Mahasseni, Behrooz
Abstract:: Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
Resource Type:: Dissertation

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Creator:: Zemicheal, Tadesse H.
Abstract:: Advances in sensor technology are greatly expanding the range of quantities that can be measured while simultaneously reducing the cost. However, deployed sensors drift out of calibration and fail, so every sensor network requires quality control procedures to promptly detect these failures. To address these problems, we propose a two-level...
Resource Type:: Dissertation

Machine learning for improving the quality of citizen science data

Creator:: Yu, Jun
Abstract:: Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially useful if the scope of the study is too broad to be performed by a limited number of trained scientists. Although citizen scientists can contribute...
Resource Type:: Dissertation

Representationally Robust and Scalable Learning over Relational Databases

Creator:: Picado Leiva, Jose Manuel
Abstract:: Learning novel concepts from relational databases is an important problem with applications in several disciplines, such as data management, natural language processing, and bioinformatics. For a learning algorithm to be effective, the input data should be clean and in some desired representation. However, real-world data is usually heterogeneous – the...
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

Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

Creator:: Raghavan, Aswin
Abstract:: Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...
Resource Type:: Dissertation

ScholarsArchive@OSU

Adaptive Movement Intent Decoding for Intuitive Control of Neuroprostheses

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

Part-based and Uncertainty-Aware Few-shot Object Segmentation in Images

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

Robust and Efficient Classification of Videos in the Wild

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Machine learning for improving the quality of citizen science data

Representationally Robust and Scalable Learning over Relational Databases

Characterizing and Addressing Dataset Shift with Deep Learning

Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

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