Artificial Intelligence and Robotics Commons^™

Panoramic Learning With A Standardized Machine Learning Formalism, Zhiting Hu, Eric P. Xing

Machine Learning Faculty Publications

Machine Learning (ML) is about computational methods that enable machines to learn concepts from experiences. In handling a wide variety of experiences ranging from data instances, knowledge, constraints, to rewards, adversaries, and lifelong interplay in an ever-growing spectrum of tasks, contemporary ML/AI research has resulted in a multitude of learning paradigms and methodologies. Despite the continual progresses on all different fronts, the disparate narrowly-focused methods also make standardized, composable, and reusable development of learning solutions difficult, and make it costly if possible to build AI agents that panoramically learn from all types of experiences. This paper presents a standardized ML …

Machine Learning In Complex Scientific Domains: Hospitalization Records, Drug Interactions, Predictive Modeling And Fairness For Class Imbalanced Data, Arghya Datta

McKelvey School of Engineering Graduate Student Theses & Dissertations

Machine learning has demonstrated potential in analyzing large, complex datasets and has become ubiquitous across many fields of scientific research. As machine learning is actively deployed in many complex and critical domains, it is essential for machine learning to engage with domain expertise to aid in knowledge discovery as well as address challenges in predictive modeling in complex domains. Domain expertise represents an essential and elaborate collection of knowledge that is often under-utilized when applying machine learning in complex domains. In this dissertation, I have addressed existing challenges regarding knowledge discovery in complex domains via engagement with domain expertise, particularly …

A Neuromorphic Machine Learning Framework Based On The Growth Transform Dynamical System, Ahana Gangopadhyay

McKelvey School of Engineering Graduate Student Theses & Dissertations

As computation increasingly moves from the cloud to the source of data collection, there is a growing demand for specialized machine learning algorithms that can perform learning and inference at the edge in energy and resource-constrained environments. In this regard, we can take inspiration from small biological systems like insect brains that exhibit high energy-efficiency within a small form-factor, and show superior cognitive performance using fewer, coarser neural operations (action potentials or spikes) than the high-precision floating-point operations used in deep learning platforms. Attempts at bridging this gap using neuromorphic hardware has produced silicon brains that are orders of magnitude …

Classification Of Explainable Artificial Intelligence Methods Through Their Output Formats, Giulia Vilone, Luca Longo

Articles

Machine and deep learning have proven their utility to generate data-driven models with high accuracy and precision. However, their non-linear, complex structures are often difficult to interpret. Consequently, many scholars have developed a plethora of methods to explain their functioning and the logic of their inferences. This systematic review aimed to organise these methods into a hierarchical classification system that builds upon and extends existing taxonomies by adding a significant dimension—the output formats. The reviewed scientific papers were retrieved by conducting an initial search on Google Scholar with the keywords “explainable artificial intelligence”; “explainable machine learning”; and “interpretable machine learning”. …

Multi-Modal Data Fusion, Image Segmentation, And Object Identification Using Unsupervised Machine Learning: Conception, Validation, Applications, And A Basis For Multi-Modal Object Detection And Tracking, Nicholas Lahaye

Computational and Data Sciences (PhD) Dissertations

Remote sensing and instrumentation is constantly improving and increasing in capability. Included within this, is the increase in amount of different instrument types, with various combinations of spatial and spectral resolutions, pointing angles, and various other instrument-specific qualities. While the increase in instruments, and therefore datasets, is a boon for those aiming to study the complexities of the various Earth systems, it can also present a large number of new challenges. With this information in mind, our group has set our aims on combining datasets with different spatial and spectral resolutions in an effective and as-general-as-possible way, with as little …

Signal Fingerprinting And Machine Learning Framework For Uav Detection And Identification., Olusiji Oloruntobi Medaiyese

Electronic Theses and Dissertations

Advancement in technology has led to creative and innovative inventions. One such invention includes unmanned aerial vehicles (UAVs). UAVs (also known as drones) are now an intrinsic part of our society because their application is becoming ubiquitous in every industry ranging from transportation and logistics to environmental monitoring among others. With the numerous benign applications of UAVs, their emergence has added a new dimension to privacy and security issues. There are little or no strict regulations on the people that can purchase or own a UAV. For this reason, nefarious actors can take advantage of these aircraft to intrude into …

Take The Lead: Toward A Virtual Video Dance Partner, Ty Farris

Master's Theses

My work focuses on taking a single person as input and predicting the intentional movement of one dance partner based on the other dance partner's movement. Human pose estimation has been applied to dance and computer vision, but many existing applications focus on a single individual or multiple individuals performing. Currently there are very few works that focus specifically on dance couples combined with pose prediction. This thesis is applicable to the entertainment and gaming industry by training people to dance with a virtual dance partner.

Many existing interactive or virtual dance partners require a motion capture system, multiple cameras …

Applying Deep Learning To The Ice Cream Vendor Problem: An Extension Of The Newsvendor Problem, Gaffar Solihu

Electronic Theses and Dissertations

The Newsvendor problem is a classical supply chain problem used to develop strategies for inventory optimization. The goal of the newsvendor problem is to predict the optimal order quantity of a product to meet an uncertain demand in the future, given that the demand distribution itself is known. The Ice Cream Vendor Problem extends the classical newsvendor problem to an uncertain demand with unknown distribution, albeit a distribution that is known to depend on exogenous features. The goal is thus to estimate the order quantity that minimizes the total cost when demand does not follow any known statistical distribution. The …

Modeling Of Argon Bombardment And Densification Of Low Temperature Organic Precursors Using Reactive Md Simulations And Machine Learning, Kwabena Asante-Boahen

Graduate Theses/Dissertations

In this study, an important aspect of the synthesis process for a-BxC:Hy was systematically modeled by utilizing the Reactive Molecular Dynamics (MD) in modeling the argon bombardment from the orthocarborane molecules as the precursor. The MD simulations are used to assess the dynamics associated with the free radicals that result from the ion bombardment. By applying the Data Mining/Machine Learning analysis into the datasets generated from the large reactive MD simulations, I was able to identify and quality the kinetics of these radicals. Overall, this approach allows for a better understanding of the overall mechanism at the atomistic level of …

Desktop Application For The Puzzle Board Game “Rush Hour”, Huanqing Nong

Electronic Theses, Projects, and Dissertations

Rush Hour is a sliding block puzzle board game. This game comes with a board of 6 x 6 grid simulating a parking lot with an exit at the right end of the third row and some vehicle models of size 1 x 2 or 1 x 3 which can slide along the grooves of the grid forward or backward. The goal of the game is to clear the path by moving the vehicles on the board in a certain way for the target car, which lies on the third row of the grid, to merge out the “parking lot” …

Forecasting Pedestrian Trajectory Using Deep Learning, Arsal Syed

UNLV Theses, Dissertations, Professional Papers, and Capstones

In this dissertation we develop different methods for forecasting pedestrian trajectories. Complete understanding of pedestrian motion is essential for autonomous agents and social robots to make realistic and safe decisions. Current trajectory prediction methods rely on incorporating historic motion, scene features and social interaction to model pedestrian behaviors. Our focus is to accurately understand scene semantics to better forecast trajectories. In order to do so, we leverage semantic segmentation to encode static scene features such as walkable paths, entry/exits, static obstacles etc. We further evaluate the effectiveness of using semantic maps on different datasets and compare its performance with already …

Learning And Exploiting Shaped Reward Models For Large Scale Multiagent Rl, Arambam James Singh, Akshat Kumar, Hoong Chuin Lau

Research Collection School Of Computing and Information Systems

Many real world systems involve interaction among large number of agents to achieve a common goal, for example, air traffic control. Several model-free RL algorithms have been proposed for such settings. A key limitation is that the empirical reward signal in model-free case is not very effective in addressing the multiagent credit assignment problem, which determines an agent's contribution to the team's success. This results in lower solution quality and high sample complexity. To address this, we contribute (a) an approach to learn a differentiable reward model for both continuous and discrete action setting by exploiting the collective nature of …

Automation Of Radiation Treatment Planning For Cervical Cancer, Dong Joo Rhee

Dissertations and Theses (Open Access)

Cervical cancer is one of the most common cancer in low- and middle-income countries (LMICs). The mortality rate can be reduced if radiation treatment becomes widely available. However, due to the lack of radiation treatment facilities and human resources, many cervical cancer patients in Africa are not able to receive timely treatments or advanced therapies. To increase the availability of radiation treatment in low-and middle-income countries (LMICs) including African countries, many attempts have been made to reduce the cost of medical linear accelerators. However, increasing the number of treatment machines would not instantly resolve the issues, as there would be …

Predicting Severity Of Traumatic Brain Injury: A Residual Learning Model From Magnetic Resonance Images, Dacosta Yeboah

Graduate Theses/Dissertations

One of the most significant frontiers for computational scientists is the engineering of human healthcare delivery based on intelligent analysis of health data. In a variety of neurological disorders such as Traumatic Brain Injury (TBI), neuro-imaging information plays a crucial role in the decision-making regarding patient care and as a potential prognostic marker for outcome. TBI is a heterogeneous neurological disorder. Due to the economic burdens of the disorder, sorting out this heterogeneity could provide more insights and better understanding of TBI recovery trajectories, thus improving overall diagnosis and treatment options. Magnetic Resonance Imaging (MRI) is a non-invasive technique that …

Rotten With Prediction, Serena Raquel Hicks

UNLV Theses, Dissertations, Professional Papers, and Capstones

This project focuses on the relationship between religion and technology as it is portrayed in Science Fiction (SF). This thesis explores the SF genre rhetorically by examining the 2002 movie Minority Report (MR), which signaled the importance of surveillance and the need to predict future crimes following 9/11. The events of 9/11 played a significant role in post 9/11 SF films, which reflect and critique our communal and cultural values. 9/11 created a new relationship between the U.S justice system, predictive technologies (PTs), and data gathering. Through the Bush Doctrine of “preemptive action,” the U.S government attempted to use Dataism, …

The Role Of Trust In Advice Acceptance From Non-Human Actors, Rahul Banerjee

Dissertations and Theses Collection (Open Access)

Advancements in technology are now allowing non-human actors in the form of robot-advisors, driverless cars, medical assistants to perform increasingly complex tasks. While technological change is as old as civilization, these non-human actors can do novel tasks. One such task is that they provide advice which is a credence service (Dulleck, & Kerschbamer, 2006). Using a financial services context this thesis studies the role trust plays in advice acceptance.

Robo-advisors are rapidly replacing human financial advisors as the agent-provider for portfolio investment services. For centuries, it was the banker (human financial advisor) who was responsible for providing his investors with …

Credit Assignment In Multiagent Reinforcement Learning For Large Agent Population, Arambam James Singh

Dissertations and Theses Collection (Open Access)

In the current age, rapid growth in sectors like finance, transportation etc., involve fast digitization of industrial processes. This creates a huge opportunity for next-generation artificial intelligence system with multiple agents operating at scale. Multiagent reinforcement learning (MARL) is the field of study that addresses problems in the multiagent systems. In this thesis, we develop and evaluate novel MARL methodologies that address the challenges in large scale multiagent system with cooperative setting. One of the key challenge in cooperative MARL is the problem of credit assignment. Many of the previous approaches to the problem relies on agent's individual trajectory which …

Splash: Learnable Activation Functions For Improving Accuracy And Adversarial Robustness, Mohammadamin Tavakoli, Forest Agostinelli, Pierre Baldi

Publications

We introduce SPLASH units, a class of learnable activation functions shown to simultaneously improve the accuracy of deep neural networks while also improving their robustness to adversarial attacks. SPLASH units have both a simple parameterization and maintain the ability to approximate a wide range of non-linear functions. SPLASH units are: (1) continuous; (2) grounded (f(0)=0"); (3) use symmetric hinges; and (4) their hinges are placed at fixed locations which are derived from the data (i.e. no learning required). Compared to nine other learned and fixed activation functions, including ReLU and its variants, SPLASH units show superior performance across three datasets …

High-Density Parking For Autonomous Vehicles., Parag J. Siddique

Electronic Theses and Dissertations

In a common parking lot, much of the space is devoted to lanes. Lanes must not be blocked for one simple reason: a blocked car might need to leave before the car that blocks it. However, the advent of autonomous vehicles gives us an opportunity to overcome this constraint, and to achieve a higher storage capacity of cars. Taking advantage of self-parking and intelligent communication systems of autonomous vehicles, we propose puzzle-based parking, a high-density design for a parking lot. We introduce a novel method of vehicle parking, which leads to maximum parking density. We then propose a heuristic method …

Learn To Intervene: An Adaptive Learning Policy For Restless Bandits In Application To Preventive Healthcare, Arpita Biswas, Gaurav Aggarwal, Pradeep Varakantham, Milind Tambe

Research Collection School Of Computing and Information Systems

In many public health settings, it is important for patients to adhere to health programs, such as taking medications and periodic health checks. Unfortunately, beneficiaries may gradually disengage from such programs, which is detrimental to their health. A concrete example of gradual disengagement has been observed by an organization that carries out a free automated call-based program for spreading preventive care information among pregnant women. Many women stop picking up calls after being enrolled for a few months. To avoid such disengagements, it is important to provide timely interventions. Such interventions are often expensive and can be provided to only …