1 citations · 1 across the 5 of their papers we have counts for
5 papers
AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification
Boshko Koloski, Senja Pollak, Roberto Navigli +1
Large semantic knowledge bases are grounded in factual knowledge. However, recent approaches to dense text representations (i.e. embeddings) do not efficiently exploit these resour…
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…
Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems
Blaž Škrlj, Nir Ki-Tov, Lee Edelist +5
Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams. We introduce Drifter, an efficient and lightweight system for online feat…
OutRank: Speeding up AutoML-based Model Search for Large Sparse Data sets with Cardinality-aware Feature Ranking
Blaž Škrlj, Blaž Mramor
The design of modern recommender systems relies on understanding which parts of the feature space are relevant for solving a given recommendation task. However, real-world data set…
DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks
Jan Kralj, Blaž Škrlj, Živa Ramšak +2
Biological systems can be studied at multiple levels of information, including gene, protein, RNA and different interaction networks levels. The goal of this work is to explore how…