3 citations · 5 across the 3 of their papers we have counts for
3 papers
stat.ML2022★ 3 cited
DBCal: Density Based Calibration of classifier predictions for uncertainty quantification
Alex Hagen, Karl Pazdernik, Nicole LaHaye +1
Measurement of uncertainty of predictions from machine learning methods is important across scientific domains and applications. We present, to our knowledge, the first such techni…
stat.CO2021
Accelerated Computation of a High Dimensional Kolmogorov-Smirnov Distance
Alex Hagen, Shane Jackson, James Kahn +4
Statistical testing is widespread and critical for a variety of scientific disciplines. The advent of machine learning and the increase of computing power has increased the interes…
cs.CL2021★ 2 cited
NukeLM: Pre-Trained and Fine-Tuned Language Models for the Nuclear and Energy Domains
Lee Burke, Karl Pazdernik, Daniel Fortin +3
Natural language processing (NLP) tasks (text classification, named entity recognition, etc.) have seen revolutionary improvements over the last few years. This is due to language…