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Adaptive Prompting for Continual Relation Extraction: A Within-Task Variance Perspective
To address catastrophic forgetting in Continual Relation Extraction (CRE), many current approaches rely on memory buffers to rehearse previously learned knowledge while acquiring new tasks.
Equivariant Polynomial Functional Networks
On the other hand, parameter-sharing-based NFNs built upon equivariant linear layers exhibit lower memory consumption and faster running time, yet their expressivity is limited due to the large size of the symmetric group of the input neural networks.
Equivariant Neural Functional Networks for Transformers
This paper systematically explores neural functional networks (NFN) for transformer architectures.
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