14 citations · 20 across the 5 of their papers we have counts for
3 papers · 1 filter
Enhancing Tabular Learners with Context-Aware Semantic Embeddings
Günther Schindler, Maximilian Schambach, Johannes Höhne
While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the ric…
FlexTab: A Flexible Encoder-Decoder Architecture for In-Context Learning Across Diverse Tabular Tasks
Marek Polewczyk, Maximilian Schambach, Marco Spinaci +2
We introduce FlexTab, a flexible encoder-decoder architecture for in-context learning on tabular data that pairs a single, task-agnostic encoder with a suite of task-specific decod…
Exploring Differences Between Tabular Enterprise Data and Public Benchmarks
Myung Jun Kim, Maximilian Schambach, Frank Essenberger +2
Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks. Yet, little is known for enterprise data, w…