5 citations · 11 across the 8 of their papers we have counts for
8 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…
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…
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…
RETRO: Relation Retrofitting For In-Database Machine Learning on Textual Data
Michael Günther, Maik Thiele, Wolfgang Lehner
There are massive amounts of textual data residing in databases, valuable for many machine learning (ML) tasks. Since ML techniques depend on numerical input representations, word…
A Cost-based Storage Format Selector for Materialization in Big Data Frameworks
Rana Faisal Munir, Alberto Abelló, Oscar Romero +2
Modern big data frameworks (such as Hadoop and Spark) allow multiple users to do large-scale analysis simultaneously. Typically, users deploy Data-Intensive Workflows (DIWs) for th…
Identifying And Weighting Integration Hypotheses On Open Data Platforms
Julian Eberius, Katrin Braunschweig, Maik Thiele +1
Open data platforms such as data.gov or opendata.socrata. com provide a huge amount of valuable information. Their free-for-all nature, the lack of publishing standards and the mul…