43 citations · 111 across the 44 of their papers we have counts for
5 papers · 1 filter
KnowsTFM: Knowledge-Informed Fine-Tuning of Small Tabular Foundation Models
Boshko Koloski, Xiangjian Jiang, Senja Pollak +3
Tabular foundation models have advanced deep learning for tabular data by delivering strong default performance across many small and medium tasks. Yet in niche domains, where data…
Digging Deeper: Learning Multi-Level Concept Hierarchies
Oscar Hill, Mateo Espinosa Zarlenga, Mateja Jamnik
Although concept-based models promise interpretability by explaining predictions with human-understandable concepts, they typically rely on exhaustive annotations and treat concept…
Hierarchical Concept-based Interpretable Models
Oscar Hill, Mateo Espinosa Zarlenga, Mateja Jamnik
Modern deep neural networks remain challenging to interpret due to the opacity of their latent representations, impeding model understanding, debugging, and debiasing. Concept Embe…
Towards Spatial Transcriptomics-driven Pathology Foundation Models
Konstantin Hemker, Andrew H. Song, Cristina Almagro-Pérez +6
Spatial transcriptomics (ST) provides spatially resolved measurements of gene expression, enabling characterization of the molecular landscape of human tissue beyond histological a…
An AI Monkey Gets Grapes for Sure -- Sphere Neural Networks for Reliable Decision-Making
Tiansi Dong, Henry He, Pietro Liò +1
This paper compares three methodological categories of neural reasoning: LLM reasoning, supervised learning-based reasoning, and explicit model-based reasoning. LLMs remain unrelia…