arXiv AI
Neural Bayesian Networks for Modular and Scalable Decision-Making Under Uncertainty
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Jul 20
A Neuro-Symbolic Approach for Probabilistic Reasoning on Graph Data
arXiv:2507. 21873v2 Announce Type: replace Abstract: Graph neural networks (GNNs) excel at predictive tasks on graph-structured data but often lack the ability to incorporate symbolic domain knowledge and perform general reasoning.
arXiv AI
Jul 7
Explainable Bayesian deep learning through input-skip Latent Binary Bayesian Neural Networks
arXiv:2503. 10496v2 Announce Type: replace-cross Abstract: Modeling natural phenomena with artificial neural networks (ANNs) often provides highly accurate predictions.
arXiv Machine Learning
Jul 17
Neural Architectures for Amortized Bayesian Inference: Statistical Foundations and Empirical Assessments
arXiv:2601. 07944v2 Announce Type: replace-cross Abstract: Since the turn of the century, approximate Bayesian inference has steadily evolved as new computational techniques have been incorporated to handle increasingly complex, large-scale predictive problems.
arXiv Machine Learning
Aug 31
Joint Bayesian Inference of Graphical Structure and Parameters with a Single Generative Flow Network
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.
arXiv Machine Learning
4d ago
Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
arXiv:2610.19074v1 Announce Type: new Abstract: Reliable uncertainty quantification remains an important challenge in AI. Hierarchical Bayesian methods provide a principled framework for this by repr...
arXiv Machine Learning
Sep 15
Online Bayesian Node Classification on Inductive Graphs under Distribution Shift
arXiv:2609.13655v1 Announce Type: new Abstract: On evolving graphs, node classifiers must satisfy two key requirements: inductive generalization to newly arriving nodes under distribution shift and c...