a python framework to build, learn and reason about probabilistic circuits and tensor networks
How to Turn Your Knowledge Graph Embeddings into Generative Models
A curated collection of papers on probabilistic circuits, computational graphs encoding tractable probability distributions.
Squared Non-monotonic Probabilistic Circuits
PyTorch implementation for "Training and Inference on Any-Order Autoregressive Models the Right Way", NeurIPS 2022 Oral, TPM 2023 Best Paper Honorable Mention
headquarters of the April team in Edinburgh
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
Headquarters of the APRIL research lab
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