55 citations · 186 across the 31 of their papers we have counts for
4 papers · 1 filter
When to Accept Automated Predictions and When to Defer to Human Judgment?
Daniel Sikar, Artur Garcez, Tillman Weyde +2
Ensuring the reliability and safety of automated decision-making is crucial. It is well-known that data distribution shifts in machine learning can produce unreliable outcomes. Thi…
The Misclassification Likelihood Matrix: Some Classes Are More Likely To Be Misclassified Than Others
Daniel Sikar, Artur Garcez, Robin Bloomfield +6
This study introduces the Misclassification Likelihood Matrix (MLM) as a novel tool for quantifying the reliability of neural network predictions under distribution shifts. The MLM…
OWL2Vec4OA: Tailoring Knowledge Graph Embeddings for Ontology Alignment
Sevinj Teymurova, Ernesto Jiménez-Ruiz, Tillman Weyde +1
Ontology alignment is integral to achieving semantic interoperability as the number of available ontologies covering intersecting domains is increasing. This paper proposes OWL2Vec…
Derivative-based regularization for regression
Enrico Lopedoto, Maksim Shekhunov, Vitaly Aksenov +2
In this work, we introduce a novel approach to regularization in multivariable regression problems. Our regularizer, called DLoss, penalises differences between the model's derivat…