Index Catalog // ScholarsArchive@OSU

Multiagent learning for locomotion and coordination in tensegrity robotics

Creator:: Iscen, Atil
Abstract:: Tensegrity structures are composed of pure compressional elements that are connected via a network of pure tensional elements. The concept of tensegrity promises numerous advantages to the field of robotics. Tensegrity robots are, however, notoriously difficult to control due to their oscillatory nature and nonlinear interaction between the components. Multiagent...
Resource Type:: Dissertation

Growth and characterization of zinc ferrite thin films for high frequency applications

Creator:: Hu, Jiqing
Abstract:: Ferrites have been used for various high frequency applications as bulk materials. These applications, however, are limited to large dimension devices. In this thesis, thin film ferrites were deposited from a low temperature solution-based deposition process that is suitable for micro-scale high frequency applications. The low temperature nature of this...
Resource Type:: Dissertation

Leveraging Structures of the Data in Deep Learning

Creator:: Li, Xingyi
Abstract:: The performance of deep learning frameworks could be significantly improved through considering the particular underlying structures for each dataset. In this thesis, I summarize our three work about boosting the performance of deep learning models through leveraging structures of the data. In the first work, we theoretically justify that, for...
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

Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

Creator:: Lei, Peng
Abstract:: This dissertation addresses the problem of video labeling at both the frame and pixel levels using deep learning. For pixel-level video labeling, we have studied two problems: i) Spatiotemporal video segmentation and ii) Boundary detection and boundary flow estimation. For the problem of spatiotemporal video segmentation, we have developed recurrent...
Resource Type:: Dissertation

Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

Creator:: Yao, Yao
Abstract:: We describe a series of novel computational models, CERENKOV (Computational Elucidation of the REgulatory NonKOding Variome) and its successors CERENKOV2, CERENKOV3, and Convolutional CERENKOV3, for discriminating regulatory single nucleotide polymorphisms (rSNPs) from non-regulatory SNPs within non-coding genetic loci. The CERENKOV models are designed for recognizing rSNPs in the context of...
Resource Type:: Dissertation

Improving and Understanding Deep Models for Natural Language Comprehension

Creator:: Ghaeini, Reza
Abstract:: Natural Language Comprehension is a challenging domain of Natural Language Processing. To improve a model’s language comprehension/understanding, one approach would be to enrich the structure of the model to enhance its capability in learning the latent rules of the language. In this dissertation, we will ﬁrst introduce several deep models...
Resource Type:: Dissertation

Relational Learning over Dirty Data Using Data Constraints

Creator:: Lee, Ga Young
Abstract:: Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values and entities. Applying machine learning on dirty databases may lead to inaccurate results. Users have to spend a lot of time and effort repairing data errors...
Resource Type:: Masters Thesis

Understanding Software-2.0: A Study of Machine Learning library usage and evolution

Creator:: Dilhara, Malinda, Ketkar, Ameya, and Dig, Danny
Abstract:: Enabled by a rich ecosystem of Machine Learning (ML) libraries, programming using learned models, i.e., Software-2.0, has gained substantial adoption. However, we do not know what challenges developers encounter when they use ML libraries. With this knowledge gap, researchers miss opportunities to contribute to new research directions, tool builders do...
Resource Type:: Research Paper

Incorporating and Learning Behavior Constraints for Sequential Decision Making

Creator:: Pinto, Jervis
Abstract:: Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
Resource Type:: Dissertation
Full Text:: neighboring domains: Vikram Iyer, Madan Thangavelu, Amin Alipour, Nadia Payet, William Brendel and Forrest

ScholarsArchive@OSU

Multiagent learning for locomotion and coordination in tensegrity robotics

Growth and characterization of zinc ferrite thin films for high frequency applications

Leveraging Structures of the Data in Deep Learning

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Pixel- and Frame-level Video Labeling using Spatial and Temporal Convolutional Networks

Computational Elucidation of the Regulatory SNPs in the Non-Coding Regions of the Human Genome

Improving and Understanding Deep Models for Natural Language Comprehension

Relational Learning over Dirty Data Using Data Constraints

Understanding Software-2.0: A Study of Machine Learning library usage and evolution

Incorporating and Learning Behavior Constraints for Sequential Decision Making

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