activity
20192021
most citedAttViz: Online exploration of self-attention for transparent neural language modeling

4 citations · 9 across the 4 of their papers we have counts for

collaborators

17 papers

cs.LG2021

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…

cs.LG2020

Predicting Generalization in Deep Learning via Metric Learning -- PGDL Shared task

Sebastian Mežnar, Blaž Škrlj

The competition "Predicting Generalization in Deep Learning (PGDL)" aims to provide a platform for rigorous study of generalization of deep learning models and offer insight into t…

cs.LG2020

Ensemble- and Distance-Based Feature Ranking for Unsupervised Learning

Matej Petković, Dragi Kocev, Blaž Škrlj +1

In this work, we propose two novel (groups of) methods for unsupervised feature ranking and selection. The first group includes feature ranking scores (Genie3 score, RandomForest s…

cs.LG2020

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-…

cs.CL2020

COVID-19 therapy target discovery with context-aware literature mining

Matej Martinc, Blaž Škrlj, Sergej Pirkmajer +4

The abundance of literature related to the widespread COVID-19 pandemic is beyond manual inspection of a single expert. Development of systems, capable of automatically processing…

stat.AP2020

To BAN or not to BAN: Bayesian Attention Networks for Reliable Hate Speech Detection

Kristian Miok, Blaz Skrlj, Daniela Zaharie +1

Hate speech is an important problem in the management of user-generated content. To remove offensive content or ban misbehaving users, content moderators need reliable hate speech…