Search Results for author:
Found 6 papers, 3 papers with code
Solving Marginal MAP Exactly by Probabilistic Circuit Transformations
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 explanations (MPE).
Symbolic Querying of Vector Spaces: Probabilistic Databases Meets Relational Embeddings
We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data.
On Constrained Open-World Probabilistic Databases
Increasing amounts of available data have led to a heightened need for representing large-scale probabilistic knowledge bases.
Scalable Rule Learning in Probabilistic Knowledge Bases
In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding.
Approximate Knowledge Compilation by Online Collapsed Importance Sampling
In particular, when the amount of exact inference is equally limited, collapsed compilation is competitive with the state of the art, and outperforms it on several benchmarks.
A Semantic Loss Function for Deep Learning with Symbolic Knowledge
This paper develops a novel methodology for using symbolic knowledge in deep learning.
