124 citations · 550 across the 52 of their papers we have counts for
9 papers · 1 filter
Regularized Estimation in High-Dimensional Vector Auto-Regressive Models using Spatio-Temporal Information
Zhenzhong Wang, Abolfazl Safikhani, Zhengyuan Zhu +1
A Vector Auto-Regressive (VAR) model is commonly used to model multivariate time series, and there are many penalized methods to handle high dimensionality. However in terms of spa…
Trend and Variance Adaptive Bayesian Changepoint Analysis & Local Outlier Scoring
Haoxuan Wu, Toryn L. J. Schafer, David S. Matteson
We adaptively estimate both changepoints and local outlier processes in a Bayesian dynamic linear model with global-local shrinkage priors in a novel model we call Adaptive Bayesia…
Likelihood Inference for Possibly Non-Stationary Processes via Adaptive Overdifferencing
Maryclare Griffin, Gennady Samorodnitsky, David S. Matteson
We make an observation that facilitates exact likelihood-based inference for the parameters of the popular ARFIMA model without requiring stationarity by allowing the upper bound $…
Extended Missing Data Imputation via GANs for Ranking Applications
Grace Deng, Cuize Han, David S. Matteson
We propose Conditional Imputation GAN, an extended missing data imputation method based on Generative Adversarial Networks (GANs). The motivating use case is learning-to-rank, the…
Deep Denoising For Scientific Discovery: A Case Study In Electron Microscopy
Sreyas Mohan, Ramon Manzorro, Joshua L. Vincent +6
Denoising is a fundamental challenge in scientific imaging. Deep convolutional neural networks (CNNs) provide the current state of the art in denoising natural images, where they p…
PyXtal FF: a Python Library for Automated Force Field Generation
Howard Yanxon, David Zagaceta, Binh Tang +2
We present PyXtal FF, a package based on Python programming language, for developing machine learning potentials (MLPs). The aim of PyXtal FF is to promote the application of atomi…