4 papers
Sparse Probabilistic Graph Circuits
Martin Rektoris, Milan Papež, Václav Šmídl +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…
Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs
Milan Papež, Martin Rektoris, Václav Šmídl +1
Deep generative models (DGMs) have recently demonstrated remarkable success in capturing complex probability distributions over graphs. Although their excellent performance is attr…
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
Milan Papež, Martin Rektoris, Václav Šmídl +1
Deep generative models have recently made a remarkable progress in capturing complex probability distributions over graphs. However, they are intractable and thus unable to answer…
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
Milan Papež, Martin Rektoris, Tomáš Pevný +1
Daily internet communication relies heavily on tree-structured graphs, embodied by popular data formats such as XML and JSON. However, many recent generative (probabilistic) models…