1 citations · 1 across the 3 of their papers we have counts for
4 papers
Semantic Loss Functions for Neuro-Symbolic Structured Prediction
Kareem Ahmed, Stefano Teso, Paolo Morettin +8
Structured output prediction problems are ubiquitous in machine learning. The prominent approach leverages neural networks as powerful feature extractors, otherwise assuming the in…
Neuro-Symbolic Entropy Regularization
Kareem Ahmed, Eric Wang, Kai-Wei Chang +1
In structured prediction, the goal is to jointly predict many output variables that together encode a structured object -- a path in a graph, an entity-relation triple, or an order…
Probabilistic Sufficient Explanations
Eric Wang, Pasha Khosravi, Guy Van den Broeck
Understanding the behavior of learned classifiers is an important task, and various black-box explanations, logical reasoning approaches, and model-specific methods have been propo…
Leveraging Unlabeled Data for Entity-Relation Extraction through Probabilistic Constraint Satisfaction
Kareem Ahmed, Eric Wang, Guy Van den Broeck +1
We study the problem of entity-relation extraction in the presence of symbolic domain knowledge. Such knowledge takes the form of an ontology defining relations and their permissib…