Here, we leverage recent advances in text-guided 3D shape generation, showing how to use image priors for generating 3D objects.

Rashomon sets can be large in size and complicated in structure, particularly for highly nonlinear function classes that allow complexinteraction terms, such asdecision trees.

The number of cleaned counter-examples across data sets and models is more than 30% of the total number of cleaned examples. FIM-based approaches outperform the LISSA estimator. FIM, which is difficult to store and invert. Figure 3 shows the results of the evaluation of Top Fisher, Practical Fisher and nearest neighbor (NN). As reported in the main text, Practical Fisher lags behind Top Fisher in all cases.

We tackle sequential learning under label noise in applications where a human supervisor can be queried to relabel suspicious examples.