4 citations · 18 across the 15 of their papers we have counts for
3 papers · 1 filter
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…
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Anton Thielmann, René-Marcel Kruse, Thomas Kneib +1
Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this…