The goal of this work is to design architectures for efficiently learning complete invariants of planar graphs.

The "Patient Instruction" (PI), which contains critical instructional information provided bothtocarers andtothepatient atthetimeofdischarge,isessential for the patient to manage their condition outside hospital.

In thissupplementarydocument, we first provethe theorem presented in the paper in Section 1. Section 2 provides additional evaluation metrics for StyleGAN-ADA [12], FastGAN [20], and Projected GAN, andFIDofProjected GAN onninemore datasets. Section 4 reports additional experiments. Lastly, we provide details on training configurations, hyperparameters, and compute in Section 5. The supplementary videos show interpolations between random samples of Projected GAN on all datasets. Code, models, and supplementary videos can be found on the project page https://sites.

Generative Adversarial Networks (GANs) produce high-quality images but are challenging totrain.

Vision-language pretrained models have seen remarkable success, but their application to safety-critical settings is limited by their lack of interpretability.

Reducing the variance of the gradient estimator is known to improve the convergence rate of stochastic gradient-based optimization and sampling algorithms.

In this work, we build on this framework and proposeAvare, a simple and efficient algorithm for adaptive importance sampling for finite-sum optimization and sampling with decreasing step-sizes.