7 citations · 13 across the 6 of their papers we have counts for
6 papers
Towards Reducing Data Acquisition and Labeling for Defect Detection using Simulated Data
Lukas Malte Kemeter, Rasmus Hvingelby, Paulina Sierak +2
In many manufacturing settings, annotating data for machine learning and computer vision is costly, but synthetic data can be generated at significantly lower cost. Substituting th…
DANSK and DaCy 2.6.0: Domain Generalization of Danish Named Entity Recognition
Kenneth Enevoldsen, Emil Trenckner Jessen, Rebekah Baglini
Named entity recognition is one of the cornerstones of Danish NLP, essential for language technology applications within both industry and research. However, Danish NER is inhibite…
How To Overcome Confirmation Bias in Semi-Supervised Image Classification By Active Learning
Sandra Gilhuber, Rasmus Hvingelby, Mang Ling Ada Fok +1
Do we need active learning? The rise of strong deep semi-supervised methods raises doubt about the usability of active learning in limited labeled data settings. This is caused by…
Deep Semi-Supervised Learning for Time Series Classification
Jann Goschenhofer, Rasmus Hvingelby, David Rügamer +3
While Semi-supervised learning has gained much attention in computer vision on image data, yet limited research exists on its applicability in the time series domain. In this work,…
Type B Reflexivization as an Unambiguous Testbed for Multilingual Multi-Task Gender Bias
Ana Valeria Gonzalez, Maria Barrett, Rasmus Hvingelby +2
The one-sided focus on English in previous studies of gender bias in NLP misses out on opportunities in other languages: English challenge datasets such as GAP and WinoGender highl…
The Danish Gigaword Project
Leon Strømberg-Derczynski, Manuel R. Ciosici, Rebekah Baglini +12
Danish language technology has been hindered by a lack of broad-coverage corpora at the scale modern NLP prefers. This paper describes the Danish Gigaword Corpus, the result of a f…