4 citations · 18 across the 15 of their papers we have counts for
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On the Efficiency of NLP-Inspired Methods for Tabular Deep Learning
Anton Frederik Thielmann, Soheila Samiee
Recent advancements in tabular deep learning (DL) have led to substantial performance improvements, surpassing the capabilities of traditional models. With the adoption of techniqu…
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.…
Neural Additive Image Model: Interpretation through Interpolation
Arik Reuter, Anton Thielmann, Benjamin Saefken
Understanding how images influence the world, interpreting which effects their semantics have on various quantities and exploring the reasons behind changes in image-based predicti…
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…