Here, we leverage recent advances in text-guided 3D shape generation, showing how to use image priors for generating 3D objects.

Wedesign a novel truncation operation to further reduce the computational complexity of theproposed algorithms.

This application is often needed in safety-critical domains suchashealthcare, whereestimating andcommunicating uncertainty to decision-makers iscrucial. Weintroduce apractical approach for integrating uncertainty estimation into a class of state-of-the-art neural network methods used for individual-level causal estimates. We show that our methods enable us to deal gracefully with situations of "no-overlap", common in highdimensional data, where standard applications of causal effect approaches fail.

Figure 1: (a) Conventional predictive learningthat aims to predict local attributes by masking them.(b) Conventional contrastive learningthat could maximize the similarity of dissimilar graphs perturbed from original graphs.