32 citations · 106 across the 33 of their papers we have counts for
4 papers · 1 filter
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…
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…
Generating Likely Counterfactuals Using Sum-Product Networks
Jiri Nemecek, Tomas Pevny, Jakub Marecek
The need to explain decisions made by AI systems is driven by both recent regulation and user demand. The decisions are often explainable only post hoc. In counterfactual explanati…