6 citations · 10 across the 10 of their papers we have counts for
11 papers
Embedding World Knowledge into Tabular Models: Towards Best Practices for Embedding Pipeline Design
Oksana Kolomenko, Ricardo Knauer, Erik Rodner
Embeddings are a powerful way to enrich data-driven machine learning models with the world knowledge of large language models (LLMs). Yet, there is limited evidence on how to desig…
In Search of Grandmother Cells: Tracing Interpretable Neurons in Tabular Representations
Ricardo Knauer, Erik Rodner
Foundation models are powerful yet often opaque in their decision-making. A topic of continued interest in both neuroscience and artificial intelligence is whether some neurons beh…
Is Visual in-Context Learning for Compositional Medical Tasks within Reach?
Simon Reiß, Zdravko Marinov, Alexander Jaus +4
In this paper, we explore the potential of visual in-context learning to enable a single model to handle multiple tasks and adapt to new tasks during test time without re-training.…
On the Domain Robustness of Contrastive Vision-Language Models
Mario Koddenbrock, Rudolf Hoffmann, David Brodmann +1
In real-world vision-language applications, practitioners increasingly rely on large, pretrained foundation models rather than custom-built solutions, despite limited transparency…
Robust Weight Imprinting: Insights from Neural Collapse and Proxy-Based Aggregation
Justus Westerhoff, Golzar Atefi, Mario Koddenbrock +4
The capacity of foundation models allows for their application to new, unseen tasks. The adaptation to such tasks is called transfer learning. An efficient transfer learning method…
Feedback-driven object detection and iterative model improvement
Sönke Tenckhoff, Mario Koddenbrock, Erik Rodner
Automated object detection has become increasingly valuable across diverse applications, yet efficient, high-quality annotation remains a persistent challenge. In this paper, we pr…
PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
Ricardo Knauer, Marvin Grimm, Erik Rodner
In practice, we are often faced with small-sized tabular data. However, current tabular benchmarks are not geared towards data-scarce applications, making it very difficult to deri…
"Oh LLM, I'm Asking Thee, Please Give Me a Decision Tree": Zero-Shot Decision Tree Induction and Embedding with Large Language Models
Ricardo Knauer, Mario Koddenbrock, Raphael Wallsberger +5
Large language models (LLMs) provide powerful means to leverage prior knowledge for predictive modeling when data is limited. In this work, we demonstrate how LLMs can use their co…
Squeezing Lemons with Hammers: An Evaluation of AutoML and Tabular Deep Learning for Data-Scarce Classification Applications
Ricardo Knauer, Erik Rodner
Many industry verticals are confronted with small-sized tabular data. In this low-data regime, it is currently unclear whether the best performance can be expected from simple base…
Cost-Sensitive Best Subset Selection for Logistic Regression: A Mixed-Integer Conic Optimization Perspective
Ricardo Knauer, Erik Rodner
A key challenge in machine learning is to design interpretable models that can reduce their inputs to the best subset for making transparent predictions, especially in the clinical…
CAD Models to Real-World Images: A Practical Approach to Unsupervised Domain Adaptation in Industrial Object Classification
Dennis Ritter, Mike Hemberger, Marc Hönig +3
In this paper, we systematically analyze unsupervised domain adaptation pipelines for object classification in a challenging industrial setting. In contrast to standard natural obj…