5 papers
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…
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…
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
Milan Papež, Martin Rektoris, Václav Å mÃdl +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…
Malicious Internet Entity Detection Using Local Graph Inference
Simon Mandlik, Tomas Pevny, Vaclav Smidl +1
Detection of malicious behavior in a large network is a challenging problem for machine learning in computer security, since it requires a model with high expressive power and scal…