Despite the rising popularity of message-passing neural networks (MPNNs), their ability to fit complex functions over graphs is limited as node representations become more similar with increasing depth—a phenomenon known as over-smoothing. Most approaches to mitigate...
Safe navigation of self-driving cars and robots requires a precise understanding of their environment. Training data for perception systems cannot cover the wide variety of objects that may appear during deployment. Thus, reliable identification of unknown objects,...
Models with similar performances exhibit significant disagreement in the predictions of individual samples, referred to as prediction churn. Our work explores this phenomenon in graph neural networks by investigating differences between models differing only in their...
We present a method that simultaneously addresses the tasks of dynamic scene novel-view synthesis and six degree-of-freedom (6-DOF) tracking of all dense scene elements. We follow an analysis-by-synthesis framework, inspired by recent work that models scenes as a...
Obtaining strong reproducible foundation language-audio models require open datasets of sufficient scale and quality. To pre-train contrastive language-audio model we compose large-scale sound effects dataset with detailed text descriptions for each sample. Generating...
