20 citations · 67 across the 9 of their papers we have counts for
12 papers
MLFMF: Data Sets for Machine Learning for Mathematical Formalization
Andrej Bauer, Matej Petković, Ljupčo Todorovski
We introduce MLFMF, a collection of data sets for benchmarking recommendation systems used to support formalization of mathematics with proof assistants. These systems help humans…
P(Expression|Grammar): Probability of deriving an algebraic expression with a probabilistic context-free grammar
Urh Primožič, Ljupčo Todorovski, Matej Petković
Probabilistic context-free grammars have a long-term record of use as generative models in machine learning and symbolic regression. When used for symbolic regression, they generat…
Unsupervised Feature Ranking via Attribute Networks
Urh Primožič, Blaž Škrlj, Sašo Džeroski +1
The need for learning from unlabeled data is increasing in contemporary machine learning. Methods for unsupervised feature ranking, which identify the most important features in su…
GalaxAI: Machine learning toolbox for interpretable analysis of spacecraft telemetry data
Ana Kostovska, Matej Petković, Tomaž Stepišnik +8
We present GalaxAI - a versatile machine learning toolbox for efficient and interpretable end-to-end analysis of spacecraft telemetry data. GalaxAI employs various machine learning…
Discovering outliers in the Mars Express thermal power consumption patterns
Matej Petković, Luke Lucas, Tomaž Stepišnik +3
The Mars Express (MEX) spacecraft has been orbiting Mars since 2004. The operators need to constantly monitor its behavior and handle sporadic deviations (outliers) from the expect…
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…
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…
Lessons Learned from the 1st ARIEL Machine Learning Challenge: Correcting Transiting Exoplanet Light Curves for Stellar Spots
Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras +20
The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed usin…
Feature Ranking for Semi-supervised Learning
Matej Petković, Sašo Džeroski, Dragi Kocev
The data made available for analysis are becoming more and more complex along several directions: high dimensionality, number of examples and the amount of labels per example. This…
Fuzzy Jaccard Index: A robust comparison of ordered lists
Matej Petković, Blaž Škrlj, Dragi Kocev +1
We propose Fuzzy Jaccard Index (FUJI) -- a scale-invariant score for assessment of the similarity between two ranked/ordered lists. FUJI improves upon the Jaccard index by incorpor…
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…
Machine learning for predicting thermal power consumption of the Mars Express Spacecraft
Matej Petković, Redouane Boumghar, Martin Breskvar +7
The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board equipment within its pre-defined operating temperatures range. To plan and optimize the scientific ope…