In this paper by M. Steininger et al., deep learning helps to improve climate models by post-processing their outputs.
moreNews - Data Science Chair
In this paper, we combine NLP with Graph Learning to advance computational literary studies - using Tolkien's Legendarium as a case study.
more
In this paper by K. Kobs, M. Steininger, and A. Hotho, we use language to guide an image embedding process such that the resulting embedding space is focused on a desired similarity notion.
moreOne Petabyte of Cluster Storage
09/30/2022The Data Science Chair at the University of Würzburg celebrates its data storage infrastructure finally reaching the milestone of 1 PetaByte raw storage capacity.
With further expansion on the horizon for later this year, our Ceph based cluster is getting ready for our future scientific endeavours that are driven by large-scale datasets from various domains, like Natural Language Processing, Environmental Research, Recommender Systems and others.
More information about our cluster can be found at the following link:
more
In this paper, we investigate if Deep Metric Learning models are prone to background bias and test a method to alleviate such bias.
moreOur paper "Towards Explainable Occupational Fraud Detection" has been accepted at MIDAS 2022
07/22/2022
In our paper, we investigate the performance and interpretability of machine learning approaches when detecting occupational fraud in company data, finding models that give both strong performance and comprehensible decisions.
more
In our paper we use the Method of Lines together with Neural ODEs to learn spatio-temporal data arising from dynamical systems. We show that the method performs consistently well on a wide range of applications.
moreWe've created an approach for constructing synthetic ERP system data with different cases of occupational fraud to help researchers develop automated detection approaches.
moreOur paper "Semi-unsupervised Learning forTime Series Classification" got accepted @ MILETS (KDD22)
06/30/2022Our workshop paper "Semi-unsupervised Learning forTime Series Classification" got accepted @ MILETS (8th SIGKDD International Workshop on Mining and Learning from Time Series -- Deep Forecasting: Models, Interpretability, and Applications) at KDD22.
moreWe are hiring!
06/29/2022We currently have two open positions for PhD students! Check here for more information on what you can do at our chair and how to apply.
more