5 citations · 5 across the 5 of their papers we have counts for
5 papers
Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
Julius Gonsior, Tim Rieß, Anja Reusch +3
Annotating data is a time-consuming and costly task, but it is inherently required for supervised machine learning. Active Learning (AL) is an established method that minimizes hum…
Reassessing Active Learning Adoption in Contemporary NLP: A Community Survey
Julia Romberg, Christopher Schröder, Julius Gonsior +2
Supervised learning relies on data annotation which usually is time-consuming and therefore expensive. A longstanding strategy to reduce annotation costs is active learning, an ite…
To Softmax, or not to Softmax: that is the question when applying Active Learning for Transformer Models
Julius Gonsior, Christian Falkenberg, Silvio Magino +3
Despite achieving state-of-the-art results in nearly all Natural Language Processing applications, fine-tuning Transformer-based language models still requires a significant amount…
ImitAL: Learned Active Learning Strategy on Synthetic Data
Julius Gonsior, Maik Thiele, Wolfgang Lehner
Active Learning (AL) is a well-known standard method for efficiently obtaining annotated data by first labeling the samples that contain the most information based on a query strat…
ImitAL: Learning Active Learning Strategies from Synthetic Data
Julius Gonsior, Maik Thiele, Wolfgang Lehner
One of the biggest challenges that complicates applied supervised machine learning is the need for huge amounts of labeled data. Active Learning (AL) is a well-known standard metho…