2 citations · 2 across the 3 of their papers we have counts for
5 papers
From Uniform to Learned Knots: A Study of Spline-Based Numerical Encodings for Tabular Deep Learning
Manish Kumar, Anton Frederik Thielmann, Christoph Weisser +1
Numerical preprocessing remains a critical component of tabular deep learning, as the representation of continuous features can strongly affect downstream performance. We systemati…
EviNAM: Intelligibility and Uncertainty via Evidential Neural Additive Models
Sören Schleibaum, Anton Frederik Thielmann, Julian Teusch +2
Intelligibility and accurate uncertainty estimation are crucial for reliable decision-making. In this paper, we propose EviNAM, an extension of evidential learning that integrates…
Mambular: A Sequential Model for Tabular Deep Learning
Anton Frederik Thielmann, Manish Kumar, Christoph Weisser +3
The analysis of tabular data has traditionally been dominated by gradient-boosted decision trees (GBDTs), known for their proficiency with mixed categorical and numerical features.…
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…
GPTopic: Dynamic and Interactive Topic Representations
Arik Reuter, Bishnu Khadka, Anton Thielmann +3
Topic modeling seems to be almost synonymous with generating lists of top words to represent topics within large text corpora. However, deducing a topic from such list of individua…