From the 1 of 7 linked papers with an AI index.
7 papers
Do Tabular Foundation Models Agree with Themselves?
Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme +1
Tabular Foundation Models (TFMs) are currently the best approach to tabular prediction problems. They are constructed as transformers that approximate the Bayesian posterior predic…
Prune, Update and Trim: Robust Structured Pruning for Large Language Models
Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidt-Thieme
The paper introduces Putri, a post‑training pruning method for large language models that updates remaining weights, prunes feed‑forward layers sequentially, and removes individual…
The Importance of Encoder Choice:A Tabular-Image Study
Ilia Koloiarov, Diego Coello de Portugal Mecke, Vijaya Krishna Yalavarthi +2
Multimodal learning usually requires a dedicated encoder per modality. When a tabular modality is involved, prior work has been mostly using a \emph{plain MLP} as the encoder. Yet…
Valid and Expressive Copulas for Irregular Multivariate Time Series
Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1
We introduce CopFITi, a copula model for probabilistic forecasting of irregular multivariate time series (IMTS). Our model combines the expressivity of normalizing flows for univar…
LAtte: Hyperbolic Lorentz Attention for Cross-Subject EEG Classification
Ahmad Bdeir, Johannes Burchert, Tom Hanika +2
Electroencephalogram (EEG) classification plays a key role in medical diagnosis and brain-computer interfaces, but remains challenging due to low signal-to-noise ratios and high in…
Conceptual Views of Neural Networks: A Framework for Neuro-Symbolic Analysis
Johannes Hirth, Tom Hanika
We introduce \emph{conceptual views} as a formal framework grounded in Formal Concept Analysis for globally explaining neural networks. Experiments on twenty-four ImageNet models a…