Browsing by Subject "Artificial intelligence"
Now showing items 1-20 of 29
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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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
Essays on Identification and Promotion of Game-Theoretic Cooperation
(2018)This dissertation looks at how to identify and promote cooperation in a multiagent system, first theoretically through the lens of computational game theory and later empirically through a human subject experiment. Chapter ... -
Ethics of Artificial Intelligence, Robotics and Supra-Intelligence
(2020)All things were created by Him and for Him:Ethics of Artificial Intelligence, Robotics and Supra-IntelligenceFascination with automation has captured the human imagination for thousands of years. As far back as 800 CE, when ... -
Indirect Training Algorithms for Spiking Neural Networks based on Spiking Timing Dependent Plasticity and Their Applications
(2017)Spiking neural networks have been used to investigate the mechanisms of processingin biological neural circuits or to propose hypotheses that can be tested in exper-iments. Because of their biological plausibility and event-based ... -
Learning Features for Unsupervised Learning and Reinforcement Learning
(2018)Feature learning has been one of the main tasks in machine learning, and the recent advancement in deep learning only increases the importance of understanding the role of features, and designing efficient algorithms to ... -
Machine Learning-based Techniques to Address Spectral Distortions in Photon Counting X-ray Computed Tomography
(2016)Spectral CT using a photon counting x-ray detector (PCXD) shows great potential for measuring material composition based on energy dependent x-ray attenuation. Spectral CT is especially suited for imaging with K-edge contrast ... -
Methods for Comparative Analysis of Chromatin Accessibility and Gene Expression, With Applications to Cellular Reprogramming
(2018)Cellular reprogramming processes remain poorly characterized at the level of genome- wide chromatin and gene expression changes. Specifically, the extent to which re- programmed cells differ quantitatively from both the ... -
Neuromagnetic Fields and Brain-Inspired Hybrid Analog-Digital Computation
(2018)Brain-inspired computing architectures such as neural networks and neuromorphic chips have demonstrated promise in performing complex pattern recognition tasks by coarsely mimicking synaptic activity in software and hardware. ... -
Online Learning of Non-Stationary Networks, with Application to Financial Data
(2012)In this paper, we propose a new learning algorithm for non-stationary Dynamic Bayesian Networks is proposed. Although a number of effective learning algorithms for non-stationary DBNs have previously been proposed and applied ...
