5 citations · 12 across the 21 of their papers we have counts for
5 papers · 2 filters
Complex KDA: Understanding and Enhancing the Expressivity of Kimi Delta Attention
Julien Siems, Riccardo Grazzi, Korbinian Pöppel +8
Linear RNNs based on the delta-rule enable efficient sequence modeling, but their linear updates with a low-rank correction constrain their expressivity. Prior work has shown that…
TimEE: End-to-end Time Series Classification via In-Context Learning
Jaris Küken, Shi Bin Hoo, Martin Mráz +2
Time series classification (TSC) is dominated by a two-stage paradigm: train a feature encoder -- either from scratch on the target dataset or via pretraining on large corpora -- a…
Towards Evaluating Data Priors for Tabular Foundation Models
Zeynep Türkmen, Kürşat Kaya, Alexander Pfefferle +1
Data-generating priors are a central component of tabular foundation models because they define the task distribution used during pretraining. However, priors are rarely evaluated…
Towards Pretraining Text Encoders for TabPFN
Mustafa Tajjar, Alexander Pfefferle, Lennart Purucker +1
Tabular foundation models, such as TabPFN, achieve strong performance on tabular datasets with numerical and categorical data, but do not natively handle high-cardinality text feat…
Causal Data Augmentation for Robust Fine-Tuning of Tabular Foundation Models
Magnus Bühler, Lennart Purucker, Frank Hutter
Fine-tuning tabular foundation models (TFMs) under data scarcity is challenging, as early stopping on even scarcer validation data often fails to capture true generalization perfor…