70 citations · 81 across the 4 of their papers we have counts for
5 papers
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
YooJung Choi, Tal Friedman, Guy Van den Broeck
Probabilistic circuits (PCs) are a class of tractable probabilistic models that allow efficient, often linear-time, inference of queries such as marginals and most probable explana…
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings
Tal Friedman, Guy Van den Broeck
We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data. We formali…
On Constrained Open-World Probabilistic Databases
Tal Friedman, Guy Van den Broeck
Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases. One approach is to use a probabilistic database, a mo…
Approximate Knowledge Compilation by Online Collapsed Importance Sampling
Tal Friedman, Guy Van den Broeck
We introduce collapsed compilation, a novel approximate inference algorithm for discrete probabilistic graphical models. It is a collapsed sampling algorithm that incrementally sel…
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
Jingyi Xu, Zilu Zhang, Tal Friedman +2
This paper develops a novel methodology for using symbolic knowledge in deep learning. From first principles, we derive a semantic loss function that bridges between neural output…