Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: the trace of the Hessian of the log-likelihood. Recent work regularizes this trace globally to bias learning toward flatter, better generalizing optima.
arXiv:2603. 11946v2 Announce Type: replace-cross Abstract: Probabilistic circuits (PCs) enable exact and tractable inference but employ data independent mixture weights that limit their ability to capture local geometry of the data manifold.
By Sahil Sidheekh, Sriraam Natarajan
arXiv:2608. 12869v1 Announce Type: cross Abstract: Probabilistic Circuits (PCs) are generative models that support exact inference and, unlike deep neural networks, admit an exact and tractable measure of loss-surface curvature: the trace of the Hessian of the log-likelihood.
By Hrithik Suresh, Sahil Sidheekh, Shelar Parth Vijay, Yasir Z, Sriraam Natarajan, Narayanan Chatapuram Krishnan
The paper introduces JSP-GFN, a Generative Flow Network that jointly infers the structure and parameters of a Bayesian Network. It sequentially generates a directed acyclic graph edge by edge and then samples the corresponding conditional probability parameters once the full structure is known. Experiments on simulated and real data show that JSP‑GFN accurately approximates the joint posterior and outperforms existing methods.
By Tristan Deleu, Mizu Nishikawa-Toomey, Jithendaraa Subramanian, Esmeralda S. Whitammer, Laurent Charlin, Yoshua Bengio
arXiv:2607. 19126v1 Announce Type: cross Abstract: Neural Markov Logic Networks (NMLNs) are a flexible neurosymbolic relational model.
By Peter Jung, Giuseppe Marra, Ondrej Kuzelka
arXiv:2608. 16565v1 Announce Type: new Abstract: This cumulative habilitation thesis studies probabilistic circuits (PCs) as a powerful and tractable framework for reasoning and learning under uncertainty in artificial intelligence (AI).
By Robert Peharz