4 citations · 10 across the 4 of their papers we have counts for
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Link Analysis meets Ontologies: Are Embeddings the Answer?
Sebastian Mežnar, Matej Bevec, Nada Lavrač +1
The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size. The development…
ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings
Blaž Škrlj, Sašo Džeroski, Nada Lavrač +1
Feature ranking has been widely adopted in machine learning applications such as high-throughput biology and social sciences. The approaches of the popular Relief family of algorit…
SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations
Sebastian Mežnar, Nada Lavrač, Blaž Škrlj
Learning from complex real-life networks is a lively research area, with recent advances in learning information-rich, low-dimensional network node representations. However, state-…
Propositionalization and Embeddings: Two Sides of the Same Coin
Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja
Data preprocessing is an important component of machine learning pipelines, which requires ample time and resources. An integral part of preprocessing is data transformation into t…
Feature Importance Estimation with Self-Attention Networks
Blaž Škrlj, Sašo Džeroski, Nada Lavrač +1
Black-box neural network models are widely used in industry and science, yet are hard to understand and interpret. Recently, the attention mechanism was introduced, offering insigh…
Symbolic Graph Embedding using Frequent Pattern Mining
Blaz Škrlj, Jan Kralj, Nada Lavrač
Relational data mining is becoming ubiquitous in many fields of study. It offers insights into behaviour of complex, real-world systems which cannot be modeled directly using propo…