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Found 5 papers, 1 papers with code
Synthesis parameter effect detection using quantitative representations and high dimensional distribution distances
Detection of effects of the parameters of the synthetic process on the microstructure of materials is an important, yet elusive goal of materials science.
Reduction of detection limit and quantification uncertainty due to interferent by neural classification with abstention
Often, detection of the physical phenomenon (termed signal) is difficult to distinguish from naturally occurring phenomena (termed background).
DBCal: Density Based Calibration of classifier predictions for uncertainty quantification
Measurement of uncertainty of predictions from machine learning methods is important across scientific domains and applications.
Accelerated Computation of a High Dimensional Kolmogorov-Smirnov Distance
We perform power analysis of ddKS and its approximations on a corpus of datasets and compare to other common high dimensional two sample tests and distances: Hotelling's T^2 test and Kullback-Leibler divergence.
Scaling the training of particle classification on simulated MicroBooNE events to multiple GPUs
The studies are carried out on simulated events in the MicroBooNE detector.
