Search Results for author:
Found 2 papers, 1 papers with code
Quantifying Distribution Shifts and Uncertainties for Enhanced Model Robustness in Machine Learning Applications
Distribution shifts, where statistical properties differ between training and test datasets, present a significant challenge in real-world machine learning applications where they directly impact model generalization and robustness.
Point Cloud Data Simulation and Modelling with Aize Workspace
This work takes a look at data models often used in digital twins and presents preliminary results specifically from surface reconstruction and semantic segmentation models trained using simulated data.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
