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Milan Papez

2 accepted papers

2025

Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs

UAI 2025

Deep generative models (DGMs) have recently demonstrated remarkable success in capturing complex probability distributions over graphs. Although their excellent performance is attributed to powerful and scalable deep neural networks, it is, at the same time, exactly the presence of these highly non-

2024

Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs

ICLR 2024poster

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 utilize neural networks to learn a probability distribution over undirected cyclic graphs. This assumption of a generic…