4 citations · 8 across the 21 of their papers we have counts for
6 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…
LLM Embeddings for Deep Learning on Tabular Data
Boshko Koloski, Andrei Margeloiu, Xiangjian Jiang +3
Tabular deep-learning methods require embedding numerical and categorical input features into high-dimensional spaces before processing them. Existing methods deal with this hetero…
HorNets: Learning from Discrete and Continuous Signals with Routing Neural Networks
Boshko Koloski, Nada Lavrač, Blaž Škrlj
Construction of neural network architectures suitable for learning from both continuous and discrete tabular data is a challenging research endeavor. Contemporary high-dimensional…
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain
Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna +70
This paper describes the 2nd edition of the ICML Topological Deep Learning Challenge that was hosted within the ICML 2024 ELLIS Workshop on Geometry-grounded Representation Learnin…
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…