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Louis-Pascal A. C. Xhonneux

2 accepted papers

2021

How to transfer algorithmic reasoning knowledge to learn new algorithms?

NeurIPS 2021poster

Learning to execute algorithms is a fundamental problem that has been widely studied. Prior work (Veličković et al., 2019) has shown that to enable systematic generalisation on graph algorithms it is critical to have access to the intermediate steps of the program/algorithm. In many reasoning tasks,…

Cited by 32SourcePDFScholar
2021

Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction

NeurIPS 2021poster

Link prediction is a very fundamental task on graphs. Inspired by traditional path-based methods, in this paper we propose a general and flexible representation learning framework based on paths for link prediction. Specifically, we define the representation of a pair of nodes as the generalized sum…