In the era of big data and artificial intelligence, distributed machine learning has emerged as a promising solution to address privacy and security concerns while fostering collaboration between multiple parties. However, with the data increased in terms of...
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...
We present sustain.AI, an intelligent, context-aware recommender system that assists auditors and financial investors as well as the general public to efficiently analyze companies’ sustainability reports. The tool leverages an end-to-end trainable architecture that...
We introduce a linguistically enhanced combination of pre-training methods for transformers. The pre-training objectives include POS-tagging, synset prediction based on semantic knowledge graphs, and parent prediction based on dependency parse trees. Our approach...
