15 citations · 23 across the 8 of their papers we have counts for
4 papers · 1 filter
Scaling Experiments in Self-Supervised Cross-Table Representation Learning
Maximilian Schambach, Dominique Paul, Johannes S. Otterbach
To analyze the scaling potential of deep tabular representation learning models, we introduce a novel Transformer-based architecture specifically tailored to tabular data and cross…
Curve Your Enthusiasm: Concurvity Regularization in Differentiable Generalized Additive Models
Julien Siems, Konstantin Ditschuneit, Winfried Ripken +4
Generalized Additive Models (GAMs) have recently experienced a resurgence in popularity due to their interpretability, which arises from expressing the target value as a sum of non…
Uncovering the Inner Workings of STEGO for Safe Unsupervised Semantic Segmentation
Alexander Koenig, Maximilian Schambach, Johannes Otterbach
Self-supervised pre-training strategies have recently shown impressive results for training general-purpose feature extraction backbones in computer vision. In combination with the…
Interpretable Reinforcement Learning via Neural Additive Models for Inventory Management
Julien Siems, Maximilian Schambach, Sebastian Schulze +1
The COVID-19 pandemic has highlighted the importance of supply chains and the role of digital management to react to dynamic changes in the environment. In this work, we focus on d…