6 papers
A Hybrid LSTM--Vision Transformer Architecture for Predicting HRRR Forecast Errors
David Aaron Evans, Jay C. Rothenberger, Kara J. Sulia +2
Forecast errors in high-resolution numerical weather prediction (NWP) systems are often linked to unresolved planetary boundary layer (PBL) processes, convection, terrain-induced c…
Predicting Forecast Error for the HRRR Using LSTM Neural Networks: A Comparative Study Using New York and Oklahoma State Mesonets
David Aaron Evans, Kara J. Sulia, Nick P. Bassill +3
Long Short-Term Memory (LSTM) models are trained to predict forecast errors for the High-Resolution Rapid Refresh (HRRR) model using the New York State Mesonet and Oklahoma State M…
CLPIPS: A Personalized Metric for AI-Generated Image Similarity
Khoi Trinh, Jay Rothenberger, Scott Seidenberger +2
Iterative prompt refinement is central to reproducing target images with text to image generative models. Previous studies have incorporated image similarity metrics (ISMs) as addi…
Machine Learning Detection of Road Surface Conditions: A Generalizable Model using Traffic Cameras and Weather Data
Carly Sutter, Kara J. Sulia, Nick P. Bassill +7
Transportation agencies make critical operational decisions during hazardous weather events, including assessment of road conditions and resource allocation. In this study, machine…
Meta Co-Training: Two Views are Better than One
Jay C. Rothenberger, Dimitrios I. Diochnos
In many critical computer vision scenarios unlabeled data is plentiful, but labels are scarce and difficult to obtain. As a result, semi-supervised learning which leverages unlabel…
A Review of Pseudo-Labeling for Computer Vision
Patrick Kage, Jay C. Rothenberger, Pavlos Andreadis +1
Deep neural models have achieved state of the art performance on a wide range of problems in computer science, especially in computer vision. However, deep neural networks often re…