1 code implementation • NeurIPS 2023 • Andrej Bauer, Matej Petković, Ljupčo Todorovski
The collection includes the largest Lean~4 library Mathlib, and some of the largest Agda libraries: the standard library, the library of univalent mathematics Agda-unimath, and the TypeTopology library.
1 code implementation • 20 Feb 2023 • Sebastian Mežnar, Sašo Džeroski, Ljupčo Todorovski
We empirically show that HVAE can be trained efficiently with small corpora of mathematical expressions and can accurately encode expressions into a smooth low-dimensional latent space.
no code implementations • 1 Dec 2022 • Urh Primožič, Ljupčo Todorovski, Matej Petković
We then present specific grammars for generating linear, polynomial, and rational expressions, where algorithms for calculating the probability of a given expression exist.
no code implementations • 4 Mar 2022 • Jure Brence, Dragan Mihailović, Viktor Kabanov, Ljupčo Todorovski, Sašo Džeroski, Jaka Vodeb
Noisy intermediate-scale quantum (NISQ) devices are spearheading the second quantum revolution.
no code implementations • 28 Jun 2021 • Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski, Dragi Kocev
Here, we analyze 40 MLC data sets by using 50 meta features describing different properties of the data.
no code implementations • 14 Feb 2021 • Jasmin Bogatinovski, Ljupčo Todorovski, Sašo Džeroski, Dragi Kocev
Several studies provide reviews of methods and datasets for MLC and a few provide empirical comparisons of MLC methods.
1 code implementation • 1 Dec 2020 • Jure Brence, Ljupčo Todorovski, Sašo Džeroski
Equation discovery, also known as symbolic regression, is a type of automated modeling that discovers scientific laws, expressed in the form of equations, from observed data and expert knowledge.
no code implementations • 1 Jul 2019 • Nikola Simidjievski, Ljupčo Todorovski, Juš Kocijan, Sašo Džeroski
In this paper, recent developments of the equation discovery method called process-based modeling, suited for nonlinear system identification, are elaborated and illustrated on two continuous-time case studies.
no code implementations • 21 Jun 2019 • Žiga Lukšič, Jovan Tanevski, Sašo Džeroski, Ljupčo Todorovski
This task involves numerous evaluations of a computationally expensive objective function.
no code implementations • 5 Jul 2017 • Sanda Martinčić-Ipšić, Tanja Miličić, Ljupčo Todorovski
In this study, we measure the performance of the document classifiers trained using the method of random forests for features generated the three models and their variants.