Patching Up Circuit Learning via Parameter Sharing
Probabilistic circuits (PCs) have emerged as generative models guaranteeing tractable probabilistic inference. However, tractability comes at the price of larger model sizes than in other generative models. As such, to scale PCs to high-dimensional data such as images, previous work has devised several parameter sharing techniques as well as fast evaluation protocols that consider learning PCs on…
We haven't written up this one. arXiv cs.AI has the full story — the link below goes straight to it.
