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Found 3 papers, 1 papers with code
Model-Independent Detection of New Physics Signals Using Interpretable Semi-Supervised Classifier Tests
Here we instead investigate a model-independent method that does not make any assumptions about the signal and uses a semi-supervised classifier to detect the presence of the signal in the experimental data.
Applications High Energy Physics - Phenomenology Data Analysis, Statistics and Probability
Gaussian Mixture Clustering Using Relative Tests of Fit
We consider clustering based on significance tests for Gaussian Mixture Models (GMMs).
A Generalization of Convolutional Neural Networks to Graph-Structured Data
This paper introduces a generalization of Convolutional Neural Networks (CNNs) from low-dimensional grid data, such as images, to graph-structured data.
