Search Results for author: Nada Lavrač

Found 20 papers, 9 papers with code

BERT meets Shapley: Extending SHAP Explanations to Transformer-based Classifiers

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.

AHAM: Adapt, Help, Ask, Model -- Harvesting LLMs for literature mining

no code implementations25 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.

Domain Adaptation Language Modelling +6

Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data

no code implementations27 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.

DDeMON: Ontology-based function prediction by Deep Learning from Dynamic Multiplex Networks

no code implementations8 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.

Link Analysis meets Ontologies: Are Embeddings the Answer?

1 code implementation23 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.

Anomaly Detection

Semantic Reasoning from Model-Agnostic Explanations

no code implementations29 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.

Transfer Learning for Node Regression Applied to Spreading Prediction

1 code implementation31 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.

Misinformation regression +1

ReliefE: Feature Ranking in High-dimensional Spaces via Manifold Embeddings

1 code implementation23 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

SNoRe: Scalable Unsupervised Learning of Symbolic Node Representations

1 code implementation8 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.

Node Classification Structural Node Embedding

Propositionalization and Embeddings: Two Sides of the Same Coin

2 code implementations8 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.

Relational Reasoning Vocal Bursts Valence Prediction

Feature Importance Estimation with Self-Attention Networks

no code implementations11 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.

Feature Importance

Symbolic Graph Embedding using Frequent Pattern Mining

1 code implementation29 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.

Graph Embedding Inductive logic programming

Embedding-based Silhouette Community Detection

1 code implementation17 Jul 2019 Blaž Škrlj, Jan Kralj, Nada Lavrač

Mining complex data in the form of networks is of increasing interest in many scientific disciplines.

Clustering Community Detection +1

Using Redescription Mining to Relate Clinical and Biological Characteristics of Cognitively Impaired and Alzheimer's Disease Patients

no code implementations20 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.


A framework for redescription set construction

no code implementations13 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.

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