Values are normalized for comparability of FID progression, as FID scores are not upper bounded and as such, absolute values for different networks and pretraining methods differ. To analyze the impact of the network architecture, pretraining method and training data, respectively the learned feature representations, on the construction of train-test splits and the entailed difficulties, we repeat our class swapping and removal procedure introduced in Section 3 in the main paper using different self-supervised models. Subsequently, we select train-test splits from the same iteration steps. Figure 1 compares the progression of distribution shifts based on FID scores normalized to the [0,1] interval for valid comparison. We observe that across all pretrained models, the general FID progressions and sampled train-test splits exhibit very similar learning problem difficulties, indicating that our sampling procedure is robust to the choice of readily available, state-of-the art self-supervised pretrained models.

T/P refers to "tokens" per patient, C/T to codes per "token," and # Codes to the number of unique codes present in the dataset. Missing values reflect quantities not reported in the source publication.

How close are we to the sci-fi vision of autonomous humanoid robots? I visited 11 companies in five Chinese cities to find out

By Chang Che. Read by Vincent Lai