4 citations · 6 across the 3 of their papers we have counts for
3 papers
Probabilistic Topic Modelling with Transformer Representations
Arik Reuter, Anton Thielmann, Christoph Weisser +2
Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful…
Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence
Anton Thielmann, Quentin Seifert, Arik Reuter +2
Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models simi…
Structural Neural Additive Models: Enhanced Interpretable Machine Learning
Mattias Luber, Anton Thielmann, Benjamin Säfken
Deep neural networks (DNNs) have shown exceptional performances in a wide range of tasks and have become the go-to method for problems requiring high-level predictive power. There…