ALERT: This system is being upgraded on Tuesday December 12. It will not be available
for use for several hours that day while the upgrade is in progress. Deposits to DukeSpace
will be disabled on Monday December 11, so no new items are to be added to the repository
while the upgrade is in progress. Everything should be back to normal by the end of
day, December 12.
Browsing by Subject "Artificial intelligence"
Now showing items 1-20 of 58
-
A Cell Decomposition Approach to Robotic Trajectory Planning via Disjunctive Programming
(2012)This thesis develops a novel solution method for the problem of collision-free, optimal control of a robotic vehicle in an obstacle populated environment. The technique presented combines the well established approximate ... -
A Comparison of the Attitudes of Human Resource (HR) Executives and HR Practitioners on the Use of Artificial Intelligence (AI)-Enabled Tools in Recruiting
(2022)As part of the technological growth in HR, companies are developing and adopting AI-enabled solutions for recruitment of qualified talent for a job opening. AI-enabled recruiting tools provide a variety of potential benefits ... -
A NEW ZEROTH-ORDER ORACLE FOR DISTRIBUTED AND NON-STATIONARY LEARNING
(2021)Zeroth-Order (ZO) methods have been applied to solve black-box or simulation-based optimization prroblems. These problems arise in many important applications nowa- days, e.g., generating adversarial attacks on machine learning ... -
Adaptive Planning in Changing Policies and Environments
(2023)Being able to adapt to different tasks is a staple of learning, as agents aim to generalize across different situations. Specifically, it is important for agents to adapt to the policies of other agents around them. In swarm ... -
Advancing the Design and Utility of Adversarial Machine Learning Methods
(2021)While significant progress has been made to craft Deep Neural Networks (DNNs) with super-human recognition performance, their reliability and robustness in challenging operating conditions is still a major concern. In this ... -
AN APPLICATION OF GRAPH DIFFUSION FOR GESTURE CLASSIFICATION
(2020)Reliable and widely available robotic prostheses have long been a dream of science fiction writers and researchers alike. The problem of sufficiently generalizable gesture recognition algorithms and technology remains a ... -
Artificial Intelligence Powered Direct Prediction of Linear Accelerator Machine Parameters: Towards a New Paradigm for Patient Specific Pre-Treatment QA
(2021)Purpose: Traditional pre-treatment patient specific QA is known for its high workload for physicist, ineffectiveness at identifying clinically relevant dosimetric uncertainties of treatment plans, and incompatibility with ... -
Automatic Identification of Training & Testing Data for Buried Threat Detection using Ground Penetrating Radar
(2017)Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has been investigated for landmine and subsurface threat detection. The radar is attached to front of a vehicle and collects ... -
Bayesian Learning with Dependency Structures via Latent Factors, Mixtures, and Copulas
(2016)Bayesian methods offer a flexible and convenient probabilistic learning framework to extract interpretable knowledge from complex and structured data. Such methods can characterize dependencies among multiple levels of hidden ... -
Deep Automatic Threat Recognition: Considerations for Airport X-Ray Baggage Screening
(2020)Deep learning has made significant progress in recent years, contributing to major advancements in many fields. One such field is automatic threat recognition, where methods based on neural networks have surpassed ... -
Deep Generative Models and Biological Applications
(2017)High-dimensional probability distributions are important objects in a wide variety of applications. Generative models provide an excellent manipulation method for training from rich available unlabeled data set and sampling ... -
Deep Generative Models for Image Representation Learning
(2018)Recently there has been increasing interest in developing generative models of data, offering the promise of learning based on the often vast quantity of unlabeled data. With such learning, one typically seeks to build rich, ... -
Deep Generative Models for Vision and Language Intelligence
(2018)Deep generative models have achieved tremendous success in recent years, with applications in various tasks involving vision and language intelligence. In this dissertation, I will mainly discuss the contributions that I ... -
Deep Latent-Variable Models for Natural Language Understanding and Generation
(2020)Deep latent-variable models have been widely adopted to model various types of data, due to its ability to: 1) infer rich high-level information from the input data (especially in a low-resource setting); 2) result in a ... -
Development of Imaging-Based Models for Analyzing the Spatiotemporal Function of Intervertebral Discs
(2023)Lumbar intervertebral discs (IVD) play a critical role in facilitating the mobility and load-bearing functionality of the spine. Consequently, degeneration of the IVDs has been linked to the development of low back pain ... -
Development of X-ray Fan Beam Coded Aperture Diffraction Imaging for Improving Breast Cancer Diagnostics
(2021)X-ray imaging technology has been used for a multitude of medical applications over the years. The typically measured X-ray transmission data, which records shape and density information by measuring the differences in X-ray ... -
Efficient and Scalable Deep Learning
(2019)Deep Neural Networks (DNNs) can achieve accuracy superior to traditional machine learning models, because of their large learning capacity and the availability of large amounts of labeled data. In general, larger DNNs can ... -
Efficient Deep Learning for Image Applications
(2020)Breakthrough of deep learning (DL) has greatly promoted development of machine learning in numerous academic disciplines and industries in recent years.A subsequent concern, which is frequently raised by multidisciplinary ... -
EFFICIENT LOW-RESOURCE TRAINING WITH PRE-TRAINED DEEP NEURAL NETWORKS
(2023)The performance of machine learning systems has been dramatically improving in recent years thanks to the advent of pre-trained deep neural networks. Such models are generally adopted as the foundation for feature extraction, ... -
Eliciting and Aggregating Information for Better Decision Making
(2018)In this thesis, we consider two classes of problems where algorithms are increasingly used to make, or assist in making, a wide range of decisions. The first class of problems we consider is the allocation of jointly owned ...
