266 citations · 739 across the 24 of their papers we have counts for
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cs.LG2020
AdapterDrop: On the Efficiency of Adapters in Transformers
Andreas Rücklé, Gregor Geigle, Max Glockner +4
Massively pre-trained transformer models are computationally expensive to fine-tune, slow for inference, and have large storage requirements. Recent approaches tackle these shortco…
cs.LG2018★ 33 cited
Why Comparing Single Performance Scores Does Not Allow to Draw Conclusions About Machine Learning Approaches
Nils Reimers, Iryna Gurevych
Developing state-of-the-art approaches for specific tasks is a major driving force in our research community. Depending on the prestige of the task, publishing it can come along wi…