2 papers
cs.LG2026
Efficient numeracy in language models through single-token number embeddings
Linus Kreitner, Paul Hager, Jonathan Mengedoht +3
To drive progress in science and engineering, large language models (LLMs) must be able to process large amounts of numerical data and solve long calculations efficiently. This is…
cs.CV2025
Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations
Laurin Lux, Alexander H. Berger, Maria Romeo Tricas +8
Interpretability is crucial to enhance trust in machine learning models for medical diagnostics. However, most state-of-the-art image classifiers based on neural networks are not i…