266 citations · 1.8k across the 115 of their papers we have counts for
4 papers · 1 filter
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…
Scalable Bayesian Preference Learning for Crowds
Edwin Simpson, Iryna Gurevych
We propose a scalable Bayesian preference learning method for jointly predicting the preferences of individuals as well as the consensus of a crowd from pairwise labels. Peoples' o…
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…
Large-scale Multi-label Text Classification - Revisiting Neural Networks
Jinseok Nam, Jungi Kim, Eneldo Loza Mencía +2
Neural networks have recently been proposed for multi-label classification because they are able to capture and model label dependencies in the output layer. In this work, we inves…