Clustering is an important technique for identifying structural information in large-scale data analysis, where the underlying dataset may be too large to store.

Combining motor obtain in Alg.2.

In the hospital, patients may be in the ICU with ECG/PPG sensors to monitor their already-poor healthcondition. ML methods must rely onlearning toimpute missing signals based onthesignal that is present, rather than learning tocreate ageneral-purpose imputation template thatmimics standard healthybehavior. Likewise, participant movement inboth contexts can result in artifacts(e.g. Inabroadercontext, we want to match the high quality level of other datasets such as PTB-XL, in which 77.01% of thesignal data areofhighest assessed quality [18]. See below for examples of ECG signals with their associated periodogram.

The promise of mobile health (mHealth) is the ability to use wearable sensors to monitor participant physiology athigh frequencies during daily life to enable temporally-precise health interventions.

Weaddress these limitations byproposing a Probabilistic Transformer1 (ProbTransformer) that models a hierarchical latent distribution and performs sampling in the latent space.

Subsequently, we select train-test splits from the same iteration steps. These settings are used throughout our study. For the few-shot experiments, the same pipeline parameters were utilized with changes noted in the respectivesection. However,thefactthatFIDscores are relatively close to another despite large semantic differences between datasets may indicate that FID based on our utilised FID estimator (Sec. Beyond these limits, generic representations learned byself-supervised learning may offerbetter zero-shot generalization,asalsodiscussedonSec.

However, common evaluation protocols only test a single, fixed data split in which train and test classes are assigned randomly. More realistic evaluations should consider abroad spectrum of distribution shifts with potentially varying degree and difficulty. In this work, we systematically construct train-test splits of increasing difficulty and present the ooDML benchmark to characterize generalization underout-of-distribution shifts inDML.ooDMLis