1 citations · 1 across the 3 of their papers we have counts for
3 papers
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…
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…