4 citations · 18 across the 15 of their papers we have counts for
15 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…
Aligning Recommendations with User Popularity Preferences
Mona Schirmer, Anton Thielmann, Pola Schwöbel +4
Popularity bias is a pervasive problem in recommender systems, where recommendations disproportionately favor popular items. This not only results in "rich-get-richer" dynamics and…
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…
LLM-Augmented Changepoint Detection: A Framework for Ensemble Detection and Automated Explanation
Fabian Lukassen, Christoph Weisser, Michael Schlee +5
This paper introduces a novel changepoint detection framework that combines ensemble statistical methods with Large Language Models (LLMs) to enhance both detection accuracy and th…
Beyond Black-Box Predictions: Identifying Marginal Feature Effects in Tabular Transformer Networks
Anton Thielmann, Arik Reuter, Benjamin Saefken
In recent years, deep neural networks have showcased their predictive power across a variety of tasks. Beyond natural language processing, the transformer architecture has proven e…
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…
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…
Human in the loop: How to effectively create coherent topics by manually labeling only a few documents per class
Anton Thielmann, Christoph Weisser, Benjamin Säfken
Few-shot methods for accurate modeling under sparse label-settings have improved significantly. However, the applications of few-shot modeling in natural language processing remain…
Community-Detection via Hashtag-Graphs for Semi-Supervised NMF Topic Models
Mattias Luber, Anton Thielmann, Christoph Weisser +1
Extracting topics from large collections of unstructured text-documents has become a central task in current NLP applications and algorithms like NMF, LDA as well as their generali…