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.
Sparse Circuits on the GPU (ICLR2025)
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
gasp! - GPU Accelerated Simplical Polynomial !ntegrator
headquarters of the april lab in Edinburgh
Probabilistic Graph Circuits: Deep Generative Models for Tractable Probabilistic Inference over Graphs
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
Headquarters of the APRIL research lab
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