4 citations · 4 across the 4 of their papers we have counts for
4 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…
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…
Accurate and Efficient Time Series Matching by Season- and Trend-aware Symbolic Approximation -- Extended Version Including Additional Evaluation and Proofs
Lars Kegel, Claudio Hartmann, Maik Thiele +1
Processing and analyzing time series data\-sets have become a central issue in many domains requiring data management systems to support time series as a native data type. A crucia…
Machine Learning-based Cardinality Estimation in DBMS on Pre-Aggregated Data
Lucas Woltmann, Claudio Hartmann, Dirk Habich +1
Cardinality estimation is a fundamental task in database query processing and optimization. As shown in recent papers, machine learning (ML)-based approaches can deliver more accur…