M Arratia, A Butter, M Campanelli, V Croft… - Journal of …, 2022 - iopscience.iop.org
Abstract Machine learning tools have empowered a qualitatively new way to perform differential cross section measurements whereby the data are unbinned, possibly in many …
The performance of machine learning algorithms depends to a large extent on the amount and the quality of data available for training. Simulations are most often used as test-beds for …
A common setting for scientific inference is the ability to sample from a high-fidelity forward model (simulation) without having an explicit probability density of the data. We propose a …
M Arratia, D Britzger, O Long… - Journal of …, 2022 - iopscience.iop.org
Most measurements in particle and nuclear physics use matrix-based unfolding algorithms to correct for detector effects. In nearly all cases, the observable is defined analogously at …
Quantification, ie, the task of training predictors of the class prevalence values in sets of unlabelled data items, has received increased attention in recent years. However, most …
There is a growing use of neural network classifiers as unbinned, high-dimensional (and variable-dimensional) reweighting functions. To date, the focus has been on marginal …
Quantification, ie, the task of predicting the class prevalence values in bags of unlabeled data items, has received increased attention in recent years. However, most quantification …
Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex …
O Long, B Nachman - The European Physical Journal C, 2024 - Springer
Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the …