Graph deep learning has recently emerged as a powerful ML concept allowing to generalize successful deep neural architectures to non-Euclidean structured data. One of the limitations of the majority ...
Modern spatial intelligence systems—SLAM pipelines, neural rendering models, and 3D scene graph frameworks—remain fragmented. Each solves part of the perception problem, but none unify: Metric ...
Niels here from the open-source team at Hugging Face. I discovered your work on Arxiv and was wondering whether you would like to submit it to hf.co/papers to improve its discoverability.If you are ...
Graph clusters (or communities) represent important graph structural information. In this paper, we present Differentiable Clustering for graph ATtention (DCAT). To the best of our knowledge, DCAT is ...
Abstract: Deep learning solutions have recently demonstrated remarkable performance in phase unwrapping by approaching the problem as a semantic segmentation task. However, these solutions lack ...
During the peer-review process the editor and reviewers write an eLife assessment that summarises the significance of the findings reported in the article (on a scale ranging from landmark to useful) ...
This important study introduces a fully differentiable variant of the Gillespie algorithm as an approximate stochastic simulation scheme for complex chemical reaction networks, allowing kinetic ...
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