3 citations · 5 across the 2 of their papers we have counts for
4 papers
A Large-Scale Sensitivity Analysis on Latent Embeddings and Dimensionality Reductions for Text Spatializations
Daniel Atzberger, Tim Cech, Willy Scheibel +3
The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on two-dimensional scatterplot layouts. These layouts result from a dime…
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization
Daniel Atzberger, Tim Cech, Willy Scheibel +4
Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorith…
Motif-Based Visual Analysis of Dynamic Networks
Eren Cakmak, Johannes Fuchs, Dominik Jäckle +3
Many data analysis problems rely on dynamic networks, such as social or communication network analyses. Providing a scalable overview of long sequences of such dynamic networks rem…
HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning
Xumeng Wang, Wei Chen, Jiazhi Xia +3
Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity amo…