no code implementations • EACL (Hackashop) 2021 • Enja Kokalj, Blaž Škrlj, Nada Lavrač, Senja Pollak, Marko Robnik-Šikonja
Transformer-based neural networks offer very good classification performance across a wide range of domains, but do not provide explanations of their predictions.
no code implementations • EACL (Hackashop) 2021 • Senja Pollak, Marko Robnik-Šikonja, Matthew Purver, Michele Boggia, Ravi Shekhar, Marko Pranjić, Salla Salmela, Ivar Krustok, Tarmo Paju, Carl-Gustav Linden, Leo Leppänen, Elaine Zosa, Matej Ulčar, Linda Freienthal, Silver Traat, Luis Adrián Cabrera-Diego, Matej Martinc, Nada Lavrač, Blaž Škrlj, Martin Žnidaršič, Andraž Pelicon, Boshko Koloski, Vid Podpečan, Janez Kranjc, Shane Sheehan, Emanuela Boros, Jose G. Moreno, Antoine Doucet, Hannu Toivonen
This paper presents tools and data sources collected and released by the EMBEDDIA project, supported by the European Union’s Horizon 2020 research and innovation program.
no code implementations • 25 Dec 2023 • Boshko Koloski, Nada Lavrač, Bojan Cestnik, Senja Pollak, Blaž Škrlj, Andrej Kastrin
Our system aims to reduce both the ratio of outlier topics to the total number of topics and the similarity between topic definitions.
no code implementations • 27 Sep 2023 • Boshko Koloski, Nada Lavrač, Senja Pollak, Blaž Škrlj
In the domain of semi-supervised learning, the current approaches insufficiently exploit the potential of considering inter-instance relationships among (un)labeled data.
no code implementations • 8 Feb 2023 • Jan Kralj, Blaž Škrlj, Živa Ramšak, Nada Lavrač, Kristina Gruden
Biological systems can be studied at multiple levels of information, including gene, protein, RNA and different interaction networks levels.
1 code implementation • 23 Nov 2021 • Sebastian Mežnar, Matej Bevec, Nada Lavrač, Blaž Škrlj
The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size.
no code implementations • 17 Oct 2021 • Blaž Škrlj, Marko Jukič, Nika Eržen, Senja Pollak, Nada Lavrač
The COVID-19 pandemic triggered a wave of novel scientific literature that is impossible to inspect and study in a reasonable time frame manually.
no code implementations • 29 Jun 2021 • Timen Stepišnik Perdih, Nada Lavrač, Blaž Škrlj
The derived semantic explanations are potentially more informative, as they describe the key attributes in the context of more general background knowledge, e. g., at the biological process level.
1 code implementation • 31 Mar 2021 • Sebastian Mežnar, Nada Lavrač, Blaž Škrlj
This work is one of the first to explore transferability of the learned representations for the task of node regression; we show there exist pairs of networks with similar structure between which the trained models can be transferred (zero-shot), and demonstrate their competitive performance.
1 code implementation • 23 Jan 2021 • Blaž Škrlj, Sašo Džeroski, Nada Lavrač, Matej Petković
The utility of ReliefE for high-dimensional data sets is ensured by its implementation that utilizes sparse matrix algebraic operations.
Multi-Label Classification Vocal Bursts Intensity Prediction
1 code implementation • 8 Sep 2020 • 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.
Ranked #17 on Node Classification on Coauthor CS
no code implementations • 30 Jul 2020 • Matej Martinc, Blaž Škrlj, Sergej Pirkmajer, Nada Lavrač, Bojan Cestnik, Martin Marzidovšek, Senja Pollak
The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert.
2 code implementations • 8 Jun 2020 • Nada Lavrač, Blaž Škrlj, Marko Robnik-Šikonja
This paper outlines some of the modern data processing techniques used in relational learning that enable data fusion from different input data types and formats into a single table data representation, focusing on the propositionalization and embedding data transformation approaches.
no code implementations • 11 Feb 2020 • Blaž Škrlj, Sašo Džeroski, Nada Lavrač, Matej Petkovič
Black-box neural network models are widely used in industry and science, yet are hard to understand and interpret.
1 code implementation • 29 Oct 2019 • Blaz Škrlj, Jan Kralj, Nada Lavrač
The proposed SGE approach on a venue classification task outperforms shallow node embedding methods such as DeepWalk, and performs similarly to metapath2vec, a black-box representation learner that can exploit node and edge types in a given graph.
1 code implementation • 17 Jul 2019 • Blaž Škrlj, Jan Kralj, Nada Lavrač
Mining complex data in the form of networks is of increasing interest in many scientific disciplines.
1 code implementation • 11 Feb 2019 • Blaž Škrlj, Jan Kralj, Janez Konc, Marko Robnik-Šikonja, Nada Lavrač
Network node embedding is an active research subfield of complex network analysis.
1 code implementation • 1 Feb 2019 • Blaž Škrlj, Matej Martinc, Jan Kralj, Nada Lavrač, Senja Pollak
The use of background knowledge is largely unexploited in text classification tasks.
no code implementations • 20 Feb 2017 • Matej Mihelčić, Goran Šimić, Mirjana Babić Leko, Nada Lavrač, Sašo Džeroski, Tomislav Šmuc
However, in some instances, as with the attributes: testosterone, the imaging attribute Spatial Pattern of Abnormalities for Recognition of Early AD, as well as the levels of leptin and angiopoietin-2 in plasma, we corroborated previously debatable findings or provided additional information about these variables and their association with AD pathogenesis.
no code implementations • 13 Jun 2016 • Matej Mihelčić, Sašo Džeroski, Nada Lavrač, Tomislav Šmuc
In contrast to previous approaches that typically create one smaller set of redescriptions satisfying a pre-defined set of constraints, we introduce a framework that creates large and heterogeneous redescription set from which user/expert can extract compact sets of differing properties, according to its own preferences.