20 citations · 158 across the 42 of their papers we have counts for
17 papers · 1 filter
Do Tabular Foundation Models Agree with Themselves?
Christian Klötergens, Vijaya Krishna Yalavarthi, Lars Schmidt-Thieme +1
Tabular Foundation Models (TFMs) are the state of the art for many tabular prediction tasks. They are transformers that approximate the Bayesian posterior predictive distribution u…
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…
Gaussian Mixture Copula Processes for Irregular Time Series
Christian Klötergens, Tom Hanika, Lars Schmidt-Thieme +1
We introduce Gaussian Mixture Copula Processes (GMCP), a conditional copula process for irregularly sampled multivariate time series (IMTS) that is expressive and marginalization c…
Prune, Update and Trim: Robust Structured Pruning for Large Language Models
Diego Coello de Portugal Mecke, Tom Hanika, Lars Schmidt-Thieme
Large Language Models (LLMs) have experienced significant growth and development in recent years. However, performing inference on LLMs remains costly, especially for long-context…
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…
What is the dimension of your binary data? -- and how to compute it quickly
Tom Hanika, Tobias Hille
Dimensionality is an important aspect for analyzing and understanding (high-dimensional) data. In their 2006 ICDM paper Tatti et al. answered the question for a (interpretable) dim…
Reproducibility and Geometric Intrinsic Dimensionality: An Investigation on Graph Neural Network Research
Tobias Hille, Maximilian Stubbemann, Tom Hanika
Difficulties in replication and reproducibility of empirical evidences in machine learning research have become a prominent topic in recent years. Ensuring that machine learning re…
Selecting Features by their Resilience to the Curse of Dimensionality
Maximilian Stubbemann, Tobias Hille, Tom Hanika
Real-world datasets are often of high dimension and effected by the curse of dimensionality. This hinders their comprehensibility and interpretability. To reduce the complexity fea…
Scaling Dimension
Bernhard Ganter, Tom Hanika, Johannes Hirth
Conceptual Scaling is a useful standard tool in Formal Concept Analysis and beyond. Its mathematical theory, as elaborated in the last chapter of the FCA monograph, still has room…
Conceptual Views on Tree Ensemble Classifiers
Tom Hanika, Johannes Hirth
Random Forests and related tree-based methods are popular for supervised learning from table based data. Apart from their ease of parallelization, their classification performance…
Intrinsic Dimension for Large-Scale Geometric Learning
Maximilian Stubbemann, Tom Hanika, Friedrich Martin Schneider
The concept of dimension is essential to grasp the complexity of data. A naive approach to determine the dimension of a dataset is based on the number of attributes. More sophistic…
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…
Quantifying the Conceptual Error in Dimensionality Reduction
Tom Hanika, Johannes Hirth
Dimension reduction of data sets is a standard problem in the realm of machine learning and knowledge reasoning. They affect patterns in and dependencies on data dimensions and ult…
Topic Space Trajectories: A case study on machine learning literature
Bastian Schäfermeier, Gerd Stumme, Tom Hanika
The annual number of publications at scientific venues, for example, conferences and journals, is growing quickly. Hence, even for researchers it becomes harder and harder to keep…
FCA2VEC: Embedding Techniques for Formal Concept Analysis
Dominik Dürrschnabel, Tom Hanika, Maximilian Stubbemann
Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding latent semantic an…
Collaborative Interactive Learning -- A clarification of terms and a differentiation from other research fields
Tom Hanika, Marek Herde, Jochen Kuhn +8
The field of collaborative interactive learning (CIL) aims at developing and investigating the technological foundations for a new generation of smart systems that support humans i…
Distances for WiFi Based Topological Indoor Mapping
Bastian Schäfermeier, Tom Hanika, Gerd Stumme
For localization and mapping of indoor environments through WiFi signals, locations are often represented as likelihoods of the received signal strength indicator. In this work we…