Browsing Faculty of Arts and Sciences by Keyword "machine learning"
Now showing items 1-20 of 36
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A Framework for Rapid Active Learning in Resource-Constrained Environmental Sensing Domains
(2022-05-23)Recent advancements in deep convolutional neural networks have enabled accurate, efficient, and intelligent feature learning for a wide variety of classification tasks. However, there remains a research to practice gap ... -
Accelerating Discovery in Virtual Chemical Libraries
(2023-05-15)High-throughput virtual screening (HTVS) is an essential workflow in molecular discovery programs that aids researchers in prioritizing compounds for experimental testing within large libraries of molecules. The application ... -
Accurate Measurements of Pointing Performance from In Situ Observations
(Association for Computing Machinery, 2012)We present a method for obtaining lab-quality measurements of pointing performance from unobtrusive observations of natural in situ interactions. Specifically, we have developed a set of user-independent classifiers for ... -
Active Learning for Improved Damage Detection and Disaster Response
(2021-06-23)From the 2020 Western Wildfires to the 2010 Haiti earthquake, each year natural disasters cost the world thousands of lives lost, trillions of dollars in damage, and irreparable long- term harm to the communities they ... -
Adapting Fairness-Intervention Algorithms to Missing Data
(2023-06-30)Missing values in real-world data pose a significant and unique challenge to algorithmic fairness. Different demographic groups may be unequally affected by missing data, and standard procedures for handling missing values ... -
Algorithms and Models for Genome Biology
(2014-02-25)New advances in genomic technology make it possible to address some of the most fundamental questions in biology for the first time. They also highlight a need for new approaches to analyze and model massive amounts of ... -
Analyzing and Evaluating Post hoc Explanation Methods for Black Box Machine Learning
(2022-05-23)Over the past decade, complex tools such as deep learning models have been increasingly employed in high-stakes domains such as healthcare and criminal justice. Furthermore, these models achieve state-of-the-art accuracy ... -
AXIS: Generating Explanations at Scale with Learnersourcing and Machine Learning
(2016)While explanations may help people learn by providing information about why an answer is correct, many problems on online platforms lack high-quality explanations. This paper presents AXIS (Adaptive eXplanation Improvement ... -
Bridging interpretable AI methods to systems biology and medical informatics
(2023-01-19)Computational modeling of biomedical systems can be used to describe and make therapeutically useful predictions of system behaviors, such as identifying potential drug targets or early detection of high-risk patients for ... -
Combination Antibiotic-Focused Machine Learning Models for Integration into Experimental Workflows
(2022-06-02)Antibiotic resistance is a growing crisis with far-reaching impacts on public and global health [1][2]. With antibiotic resistance impeding the efficacy of antibiotics and the discovery of new antibiotics slowing [2], ... -
Context-Robust Object Recognition via Object Manipulations in a Synthetic 3D Environment
(2021-06-03)The remote control is a small object that does not fly in the air and is generally found on a table, not in the sink. Such contextual regularities are ingrained in our perception of the world and previous research suggests ... -
Crowdsourcing the creation of image segmentation algorithms for connectomics
(Frontiers Media S.A., 2015)To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary ... -
Detection and Characterization of Autism Spectrum Disorder and Parkinson’s Disease Utilizing Measures of Speech- and Fine-Motor Coordination
(2023-05-02)Neurological disorders such as Autism Spectrum Disorder (ASD) and Parkinson’s Disease (PD) are typically associated with observed motor difficulties in speech production and in fine-motor tasks (including oculo-motor tasks), ... -
Diagnosis of iron deficiency anemia using density-based fractionation of red blood cells
(Royal Society of Chemistry (RSC), 2016)Iron deficiency anemia (IDA) is a nutritional disorder that impacts over one billion people worldwide, it causes permanent cognitive impairment in children, fatigue in adults, and suboptimal outcomes in pregnancy. IDA can ... -
Dynamics of Algorithmic Fairness
(2022-05-11)The rise of machine learning-based predictive models in making decisions of profound social impact has spurred study of those technical properties that may bear on the moral and political character of their deployment. One ... -
An Economist's Perspective on Multi-Agent Learning
(Elsevier, 2007)We comment on the Shoham, Powers, and Grenager survey of multi-agent learning and game theory, emphasizing that some of their categories are important for economics and others are not. We also try to correct some minor ... -
Engineering Protease Activity Sensors and Machine Learning Methods to Detect and Characterize Disease
(2021-05-11)Precision medicine promises the ability to intelligently tailor clinical interventions to individual patient needs. Personalizing clinical management will necessitate access to high quality, accurate, and functional ... -
Essays in Development Economics
(2020-09-08)This dissertation explores the political and economic determinants of why households in developing countries have different economic outcomes. The first chapter documents how different electoral rules can lead to different ... -
Exotic Transients and How to Find Them
(2021-05-12)Modern telescope surveys are finding thousands of astronomical transients every month, and thanks to their untargeted nature we have been able to discover a wide array of new classes of transients. Among these transients ... -
Exponentially Faster Submodular Maximization in Practice via Low Adaptivity Algorithms
(2024-03-12)Across machine learning, social network analysis, and algorithms, many fundamental objectives we care to optimize are submodular, such as influence, innovation diffusion, clustering, mutual information, feature selection, ...