93 citations · 216 across the 33 of their papers we have counts for
4 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…
Incremental Graph Construction Enables Robust Spectral Clustering of Texts
Marko Pranjić, Boshko Koloski, Nada Lavrač +2
Neighborhood graphs are a critical but often fragile step in spectral clustering of text embeddings. On realistic text datasets, standard -NN graphs can contain many disconnecte…
Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data
Boshko Koloski, Nada Lavrač, Senja Pollak +1
In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data. In this w…
AttViz: Online exploration of self-attention for transparent neural language modeling
Blaž Škrlj, Nika Eržen, Shane Sheehan +3
Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation. Commonly comprised…