Furthermore, this paper proposes a novel asymmetric Encoder-Decoder architecture to restore masked components, where the encoder is a Pre-trained Language Model (PLM) and the decoder is a ...

Graph Neural Networks (GNNs) have emerged as a promising tool to handle data exhibiting an irregular structure. However, most GNN architectures perform well on homophilic datasets, where the labels of ...

TLDR: We study the architecture of neural networks through the lens of network science, and discover that good neural networks are alike in terms of their underlying graph structure.. We define a ...

This is the implementation of the Graph Attention Structure-from-Motion (GASFM) architecture, presented in our CVPR 2024 paper Learning Structure-from-Motion with Graph Attention Networks. The ...