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Latent Space Representations of Neural Algorithmic Reasoners
April 30, 2024, 4:44 a.m. | Vladimir V. Mirjani\'c, Razvan Pascanu, Petar Veli\v{c}kovi\'c
cs.LG updates on arXiv.org arxiv.org
Abstract: Neural Algorithmic Reasoning (NAR) is a research area focused on designing neural architectures that can reliably capture classical computation, usually by learning to execute algorithms. A typical approach is to rely on Graph Neural Network (GNN) architectures, which encode inputs in high-dimensional latent spaces that are repeatedly transformed during the execution of the algorithm. In this work we perform a detailed analysis of the structure of the latent space induced by the GNN when executing …
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