Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous.

We focus on solving integer linear programs, and ground our approach in the large neighborhood search (LNS) paradigm, which iteratively chooses a subset of variables to optimize while leaving the remainder fixed. The appeal of LNS is that it can easily use any existing solver as a subroutine, and thus can inherit the benefits of carefully engineered heuristic or complete approaches and their software implementations.

In this section, we ablate the kernel size used to train DISCO on ImageNet. TableIshowsthats=3 achieves the best performance, which degrades fors = 5 by a significant margin (3.26%). This is consistent with the well known complexity of synthesizing images withglobalmodels, suchasGANs. For a single ImageNet image of size 224, STL requires 23.71 seconds while DISCO (K=1) only requires0.027. In this section, we list the url links that are used for training and evaluating DISCO.