Abstract: We view graph centrality algorithms as differentiable processes and explore the implications of this lens.First, we revisit PageRank, an ubiquitous graph centrality algorithm, and consider ...

Graph Neural Networks GNNs are advanced tools for graph classification, leveraging neighborhood aggregation to update node representations iteratively. This process captures local and global graph ...

Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. We propose a novel differentiable ...

Over the last decade, deep generative models have evolved to generate realistic and sharp images. The success of these models is often attributed to an extremely large number of trainable parameters ...

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 ...

Understanding the intricate relationships between visual entities in a scene is pivotal for scene comprehension. These relationships can be expressed as triplets, forming a scene graph with entities ...