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20222026
most citedMachine Learning State-of-the-Art with Uncertainties

5 citations · 9 across the 7 of their papers we have counts for

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cs.LG2025

Self-supervised denoising of raw tomography detector data for improved image reconstruction

Israt Jahan Tulin, Sebastian Starke, Dominic Windisch +2

Ultrafast electron beam X-ray computed tomography produces noisy data due to short measurement times, causing reconstruction artifacts and limiting overall image quality. To counte…

cs.LG2025

Data-efficient U-Net for Segmentation of Carbide Microstructures in SEM Images of Steel Alloys

Alinda Ezgi Gerçek, Till Korten, Paul Chekhonin +2

Understanding reactor-pressure-vessel steel microstructure is crucial for predicting mechanical properties, as carbide precipitates both strengthen the alloy and can initiate crack…

cs.LG2024

Harnessing Machine Learning for Single-Shot Measurement of Free Electron Laser Pulse Power

Till Korten, Vladimir Rybnikov, Mathias Vogt +3

Electron beam accelerators are essential in many scientific and technological fields. Their operation relies heavily on the stability and precision of the electron beam. Traditiona…

cs.LG2024

sbi reloaded: a toolkit for simulation-based inference workflows

Jan Boelts, Michael Deistler, Manuel Gloeckler +30

Scientists and engineers use simulators to model empirically observed phenomena. However, tuning the parameters of a simulator to ensure its outputs match observed data presents a…

cs.LG20225 cited

Machine Learning State-of-the-Art with Uncertainties

Peter Steinbach, Felicita Gernhardt, Mahnoor Tanveer +2

With the availability of data, hardware, software ecosystem and relevant skill sets, the machine learning community is undergoing a rapid development with new architectures and app…