Machine learning models are often personalized by usinggroup attributesthat encodepersonalcharacteristics(e.g.,sex,agegroup,HIVstatus). Insuchsettings, individuals expect to receive more accurate predictions in return for disclosing group attributes to the personalized model.

In the recent past, the seminal framework NeRF [19] inspired a lot of follow up work by modeling 3D objects as adensity functionσ(x)and view-dependent colorc(x,v)for each pointx R3 in the volume.

A summary of the covariates can be found in Table 8.

Applying image processing algorithms independently toeachvideo frame often leads to temporal inconsistencyin the resulting video.

In this paper, we revisit dense CRFs with two contributions.