3 papers
cs.LG2025
Distillation of a tractable model from the VQ-VAE
Armin HadžiÄ, Milan Papez, Tomáš Pevný
Deep generative models with discrete latent space, such as the Vector-Quantized Variational Autoencoder (VQ-VAE), offer excellent data generation capabilities, but, due to the larg…
cs.LG2025
Sparse Probabilistic Graph Circuits
Martin Rektoris, Milan Papež, Václav Å mÃdl +1
Deep generative models (DGMs) for graphs achieve impressively high expressive power thanks to very efficient and scalable neural networks. However, these networks contain non-linea…
cs.LG2025
Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs
Milan Papež, Martin Rektoris, Václav Å mÃdl +1
Deep generative models (DGMs) have recently demonstrated remarkable success in capturing complex probability distributions over graphs. Although their excellent performance is attr…