Probabilistic Circuits from the Juice library
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Updated
Jun 10, 2024 - Julia
Probabilistic Circuits from the Juice library
A Python Library for Deep Probabilistic Modeling
How to Turn Your Knowledge Graph Embeddings into Generative Models
Code for Deep Structured Mixtures of Gaussian Processes (DSMGPs)
🎲 A Kotlin DSL for probabilistic programming.
Probabilistic Circuits in Julia
Barebone slides introducing sum-product networks.
GraphSPNs: Sum-Product Networks Benefit From Canonical Orderings
Squared Non-monotonic Probabilistic Circuits
Code in support of the paper Continuous Mixtures of Tractable Probabilistic Models
PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020
Undergraduate honours project exploring learning Gaussian Mixture Models with negative components.
PyTorch implementation for "HyperSPNs: Compact and Expressive Probabilistic Circuits", NeurIPS 2021
Sum-Product-Set Networks: Deep Tractable Models for Tree-Structured Graphs
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