114 citations · 315 across the 19 of their papers we have counts for
5 papers · 1 filter
Feature construction using explanations of individual predictions
Boštjan Vouk, Matej Guid, Marko Robnik-Šikonja
Feature construction can contribute to comprehensibility and performance of machine learning models. Unfortunately, it usually requires exhaustive search in the attribute space or…
Better sampling in explanation methods can prevent dieselgate-like deception
Domen Vreš, Marko Robnik Šikonja
Machine learning models are used in many sensitive areas where besides predictive accuracy their comprehensibility is also important. Interpretability of prediction models is neces…
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…
Multiple Imputation for Biomedical Data using Monte Carlo Dropout Autoencoders
Kristian Miok, Dong Nguyen-Doan, Marko Robnik-Šikonja +1
Due to complex experimental settings, missing values are common in biomedical data. To handle this issue, many methods have been proposed, from ignoring incomplete instances to var…
AttViz: Online exploration of self-attention for transparent neural language modeling
Blaž Škrlj, Nika Eržen, Shane Sheehan +3
Neural language models are becoming the prevailing methodology for the tasks of query answering, text classification, disambiguation, completion and translation. Commonly comprised…