A variational autoencoder for n-ary treesPerčinić, Martin (Avtor) Todorovski, Ljupčo (Mentor) Mežnar, Sebastian (Komentor) neural networksvariational autoencodersgenerative modelsn-ary treesmachine learningdeep learningThe growth of generative AI and deep learning has caused an increase in the number of deep generative models that generate data of various types. Variational autoencoders (VAEs) are deep generative models, which, apart from generating data, also embed input data into a vector latent space. The hierarchical variational autoencoder (HVAE) is an autoencoder that is used for hierarchical data and can encode and decode binary trees. In this thesis, we introduce its upgrade, the hierarchical variational autoencoder nHVAE, which can encode and decode trees of arbitrary degrees. This upgrade increases the autoencoder's applicability, extending it to various fields where the data is represented with n-ary trees. The nHVAE model implements two gated recurrent units (GRUs) with the ability to encode and decode individual tree nodes of arbitrary degree. Results of the experimental comparison of nHVAE with HVAE show that the two autoencoders have similar performance. They also show that nHVAE can efficiently generate high-degree trees and is more efficient than HVAE when trained on small data sets.20242024-11-08 08:15:03Magistrsko delo/naloga164705UDK: 004.42VisID: 149618COBISS_ID: 213840131sl