4 citations · 8 across the 15 of their papers we have counts for
16 papers
Applying Machine Learning Methods to Laser Acceleration of Protons: Synthetic Data for Exploring the High Repetition Rate Regime
John J. Felice, Ronak Desai, Nathaniel Tamminga +5
Advances in ultra-intense laser technology have increased repetition rates and average power for chirped-pulse laser systems, which offers a promising solution for many application…
Block Gauss-Seidel methods for t-product tensor regression
Alejandra Castillo, Jamie Haddock, Iryna Hartsock +7
Randomized iterative algorithms, such as the randomized Kaczmarz method and the randomized Gauss-Seidel method, have gained considerable popularity due to their efficacy in solving…
Quantile-Based Randomized Kaczmarz for Corrupted Tensor Linear Systems
Alejandra Castillo, Jamie Haddock, Iryna Hartsock +7
The reconstruction of tensor-valued signals from corrupted measurements, known as tensor regression, has become essential in many multi-modal applications such as hyperspectral ima…
A non-parametric optimal design algorithm for population pharmacokinetics
Markus Hovd, Alona Kryshchenko, Michael N. Neely +3
This paper introduces a non-parametric estimation algorithm designed to effectively estimate the joint distribution of model parameters with application to population pharmacokinet…
Randomized Kaczmarz methods for t-product tensor linear systems with factorized operators
Alejandra Castillo, Jamie Haddock, Iryna Hartsock +7
Randomized iterative algorithms, such as the randomized Kaczmarz method, have gained considerable popularity due to their efficacy in solving matrix-vector and matrix-matrix regres…
Applying Machine Learning Methods to Laser Acceleration of Protons: Lessons Learned from Synthetic Data
Ronak Desai, Thomas Zhang, Ricky Oropeza +6
Researchers in the field of ultra-intense laser science are beginning to embrace machine learning methods. In this study we consider three different machine learning methods -- a t…
NPSA: Nonparametric Simulated Annealing for Global Optimization
Rong Chen, Alan Schumitzky, Alona Kryshchenko +3
In this paper we describe NPSA, the first parallel nonparametric global maximum likelihood optimization algorithm using simulated annealing (SA). Unlike the nonparametric adaptive…
Automatic Infectious Disease Classification Analysis with Concept Discovery
Elena Sizikova, Joshua Vendrow, Xu Cao +13
Automatic infectious disease classification from images can facilitate needed medical diagnoses. Such an approach can identify diseases, like tuberculosis, which remain under-diagn…
RPEM: Randomized Monte Carlo Parametric Expectation Maximization Algorithm
Rong Chen, Alan Schumitzky, Alona Kryshchenko +4
Inspired from quantum Monte Carlo, by using unbiased estimators all the time and sampling discrete and continuous variables at the same time using Metropolis algorithm, we present…
Semi-supervised Nonnegative Matrix Factorization for Document Classification
Jamie Haddock, Lara Kassab, Sixian Li +9
We propose new semi-supervised nonnegative matrix factorization (SSNMF) models for document classification and provide motivation for these models as maximum likelihood estimators.…
Semi-supervised NMF Models for Topic Modeling in Learning Tasks
Jamie Haddock, Lara Kassab, Sixian Li +9
We propose several new models for semi-supervised nonnegative matrix factorization (SSNMF) and provide motivation for SSNMF models as maximum likelihood estimators given specific d…
Sparseness-constrained Nonnegative Tensor Factorization for Detecting Topics at Different Time Scales
Lara Kassab, Alona Kryshchenko, Hanbaek Lyu +4
Temporal data (such as news articles or Twitter feeds) often consists of a mixture of long-lasting trends and popular but short-lasting topics of interest. A truly successful topic…
COVID-19 Literature Topic-Based Search via Hierarchical NMF
Rachel Grotheer, Yihuan Huang, Pengyu Li +7
A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available.…
On Large-Scale Dynamic Topic Modeling with Nonnegative CP Tensor Decomposition
Miju Ahn, Nicole Eikmeier, Jamie Haddock +7
There is currently an unprecedented demand for large-scale temporal data analysis due to the explosive growth of data. Dynamic topic modeling has been widely used in social and dat…
Nonparametric estimation of a mixing distribution for a family of linear stochastic dynamical systems
Alona Kryshchenko, Alan Schumitzky, Mike van Guilder +1
In this paper we develop a nonparametric maximum likelihood estimate of the mixing distribution of the parameters of a linear stochastic dynamical system. This includes, for exampl…
benchNGS : An approach to benchmark short reads alignment tools
Farzana Rahman, Mehedi Hassan, Alona Kryshchenko +3
In the last decade a number of algorithms and associated software have been developed to align next generation sequencing (NGS) reads with relevant reference genomes. The accuracy…