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Found 2 papers, 1 papers with code
Differentiable Earth Mover's Distance for Data Compression at the High-Luminosity LHC
In this paper, we train a convolutional neural network (CNN) to learn a differentiable, fast approximation of the EMD and demonstrate that it can be used as a substitute for computing-intensive EMD implementations.
Tag N' Train: A Technique to Train Improved Classifiers on Unlabeled Data
There has been substantial progress in applying machine learning techniques to classification problems in collider and jet physics.
High Energy Physics - Phenomenology High Energy Physics - Experiment
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