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Found 3 papers, 2 papers with code
How to transfer algorithmic reasoning knowledge to learn new algorithms?
Due to the fundamental differences between algorithmic reasoning knowledge and feature extractors such as used in Computer Vision or NLP, we hypothesise that standard transfer techniques will not be sufficient to achieve systematic generalisation.
Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs
To more effectively generalize to new relations, in this paper we study the relationships between different relations and propose to leverage a global relation graph.
Continuous Graph Neural Networks
The key idea is how to characterise the continuous dynamics of node representations, i. e. the derivatives of node representations, w. r. t.
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