Index Catalog // ScholarsArchive@OSU

Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

Creator:: Batke, Ryan
Abstract:: As bipedal robots move ever closer to being integrated into all manner of real world envi-ronments there is a necessity to push their dynamic capabilities to meet or exceed those of humans and animals. Advancements must be made to address ordinary challenges that arise everyday in the same environments that...
Resource Type:: Masters Thesis

Agile Bipedal Locomotion via Hierarchical Control by Incorporating Physical Principles, Learning, and Optimization

Creator:: Green, Kevin
Abstract:: Robotic Bipedal locomotion holds the potential for efficient, robust traversal of difficult terrain. The difficulty lies in the dynamics of locomotion which complicate control and motion planning. Bipedal locomotion dynamics are dimensionally large problems, extremely nonlinear, and operate on the limits of actuator capabilities, which limit the performance of generic...
Resource Type:: Dissertation

Counterfactual States for Atari Agents via Generative Deep Learning

Creator:: Olson, Matthew L.
Abstract:: Although deep reinforcement learning agents have produced impressive results in many domains, their decision making is difficult to explain to humans. To address this problem, past work has mainly focused on explaining why an action was chosen in a given state. A different type of explanation that is useful is...
Resource Type:: Masters Thesis
Full Text:: optimization: minimize ||Ew(A(s))− z∗w||22 subject to argmax a∈Actions π(DW (z ∗ w), a) = a ′ where s is

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
Full Text:: = {W, b}, being learnt?” The objective of the classification task is to minimize the loss with respect

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
Full Text:: PRES RELH RAIN WDSD SRAD TAIR TA9M WDIR WSSD WMAX WS2M WSPD WVEC PR ES RE LH RA IN W DS

Deep Convolutional Network on Point Clouds for 3D Scene Understanding

Creator:: Wu, Wenxuan
Abstract:: As one of the most popular data types, the point cloud is widely used in various appli- cations, including computer vision, computer graphics and robotics. The capability to directly measure 3D point clouds is invaluable in those applications as depth information could remove a lot of the segmentation ambiguities in...
Resource Type:: Dissertation
Full Text:: FGR(rigid) [180]; (d) Results from CPD(non-rigid) [99]; (e) Results from PointPWC-Net(w/o ft+Full

Statistical Properties and Guarantees for Certain Anomaly Detection Relevant Problems

Creator:: Liu, Si
Abstract:: Anomaly detection aims at detecting the points that appear different than the majority of the data, such that they are suspected to be generated from a different distribution. Anomaly detectors have been applied in many different fields, such as detecting fraudulent behaviors in bank transaction, finding broken sensors in a...
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
Full Text:: schema: advisedBy(x, y)←inPhase(x, ‘post quals’), ta(v, x, w), taughtBy(v, y, z). 10 Original Schema

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

ScholarsArchive@OSU

Agile Bipedal Maneuvers Optimized then Learned from Single Rigid-Body Model Dynamics

Agile Bipedal Locomotion via Hierarchical Control by Incorporating Physical Principles, Learning, and Optimization

Counterfactual States for Atari Agents via Generative Deep Learning

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

Anomaly Detection and Probabilistic Diagnosis for Automated Data Quality Control

Deep Convolutional Network on Point Clouds for 3D Scene Understanding

Statistical Properties and Guarantees for Certain Anomaly Detection Relevant Problems

Representationally Robust and Scalable Learning over Relational Databases

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

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