Anderson mixing (AM) is an acceleration method for fixed-point iterations.

The ability of humans to separate and localize sounds in noisy environments is a remarkable phenomenon known as the "cocktail party effect."

This motivation is particularly relevant given the perhaps surprising observation that a wide variety of pruning approaches increase test accuracy despite sometimes massive reductions in parameter counts.

Despite achieving remarkable performance, deep graph learning models, such as node classification and network embedding, suffer from harassment caused by small adversarial perturbations.

The task of scene graph generation entails identifying object entities and their corresponding interaction predicates in a given image (or video).