Zobrazeno 1 - 10
of 38
pro vyhledávání: '"Petkovic, Matej"'
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 identify which previous entries (theorems, constructions, datatypes, a
Externí odkaz:
http://arxiv.org/abs/2310.16005
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 generate algebraic expressions. We define the latter as equivalence classes o
Externí odkaz:
http://arxiv.org/abs/2212.00751
Publikováno v:
Soares C., Torgo L. (eds) Discovery Science. DS 2021. Lecture Notes in Computer Science, vol 12986. Springer, Cham
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 such data are thus gaining attention, and so are their applications in s
Externí odkaz:
http://arxiv.org/abs/2111.13273
Autor:
Škrlj, Blaž, Petkovič, Matej
Contemporary natural language processing (NLP) revolves around learning from latent document representations, generated either implicitly by neural language models or explicitly by methods such as doc2vec or similar. One of the key properties of the
Externí odkaz:
http://arxiv.org/abs/2110.07595
Autor:
Petković, Matej, Lucas, Luke, Stepišnik, Tomaž, Panov, Panče, Simidjievski, Nikola, Kocev, Dragi
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 expected patterns of measurements of quantities that the satellite is sendin
Externí odkaz:
http://arxiv.org/abs/2108.02067
Autor:
Kostovska, Ana, Petković, Matej, Stepišnik, Tomaž, Lucas, Luke, Finn, Timothy, Martínez-Heras, José, Panov, Panče, Džeroski, Sašo, Donati, Alessandro, Simidjievski, Nikola, Kocev, Dragi
Publikováno v:
8th IEEE International Conference on Space Mission Challenges for Information Technology (SMC-IT 2021)
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 algorithms for multivariate time series analyses, classification, reg
Externí odkaz:
http://arxiv.org/abs/2108.01407
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 algorithms assign importances to features by iteratively accounting for neare
Externí odkaz:
http://arxiv.org/abs/2101.09577
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 score) that are computed from ensembles of predictive clustering trees.
Externí odkaz:
http://arxiv.org/abs/2011.11679
Autor:
Nikolaou, Nikolaos, Waldmann, Ingo P., Tsiaras, Angelos, Morvan, Mario, Edwards, Billy, Yip, Kai Hou, Tinetti, Giovanna, Sarkar, Subhajit, Dawson, James M., Borisov, Vadim, Kasneci, Gjergji, Petkovic, Matej, Stepisnik, Tomaz, Al-Ubaidi, Tarek, Bailey, Rachel Louise, Granitzer, Michael, Julka, Sahib, Kern, Roman, Ofner, Patrick, Wagner, Stefan, Heppe, Lukas, Bunse, Mirko, Morik, Katharina
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 using machine learning methodology. For instance, the most prolific method
Externí odkaz:
http://arxiv.org/abs/2010.15996
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 poses a variety of challenges for the existing machine learning metho
Externí odkaz:
http://arxiv.org/abs/2008.03937