30 citations · 95 across the 9 of their papers we have counts for
Showing 2020Show all
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cs.CL2020★ 23 cited
Parameter-Efficient Transfer Learning with Diff Pruning
Demi Guo, Alexander M. Rush, Yoon Kim
While task-specific finetuning of pretrained networks has led to significant empirical advances in NLP, the large size of networks makes finetuning difficult to deploy in multi-tas…
cs.CL2020★ 3 cited
Sequence-Level Mixed Sample Data Augmentation
Demi Guo, Yoon Kim, Alexander M. Rush
Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. This work proposes a simple data augmentation approach t…
cs.CL2020★ 10 cited
Emergence of Separable Manifolds in Deep Language Representations
Jonathan Mamou, Hang Le, Miguel Del Rio +4
Deep neural networks (DNNs) have shown much empirical success in solving perceptual tasks across various cognitive modalities. While they are only loosely inspired by the biologica…