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.
1 code implementation • 23 Nov 2021 • Sebastian Mežnar, Matej Bevec, Nada Lavrač, Blaž Škrlj
The increasing amounts of semantic resources offer valuable storage of human knowledge; however, the probability of wrong entries increases with the increased size.
1 code implementation • 31 Mar 2021 • Sebastian Mežnar, Nada Lavrač, Blaž Škrlj
This work is one of the first to explore transferability of the learned representations for the task of node regression; we show there exist pairs of networks with similar structure between which the trained models can be transferred (zero-shot), and demonstrate their competitive performance.
no code implementations • 16 Dec 2020 • 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 the progress of understanding and explaining these models.
1 code implementation • 8 Sep 2020 • 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.
Ranked #17 on Node Classification on Coauthor CS