241 citations · 442 across the 12 of their papers we have counts for
10 papers · 1 filter
Neural-Symbolic Integration for Interactive Learning and Conceptual Grounding
Benedikt Wagner, Artur d'Avila Garcez
We propose neural-symbolic integration for abstract concept explanation and interactive learning. Neural-symbolic integration and explanation allow users and domain-experts to lear…
Counterfactual Instances Explain Little
Adam White, Artur d'Avila Garcez
In many applications, it is important to be able to explain the decisions of machine learning systems. An increasingly popular approach has been to seek to provide \emph{counterfac…
Neurosymbolic AI: The 3rd Wave
Artur d'Avila Garcez, Luis C. Lamb
Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry. Nevertheless, concerns about…
Layerwise Knowledge Extraction from Deep Convolutional Networks
Simon Odense, Artur d'Avila Garcez
Knowledge extraction is used to convert neural networks into symbolic descriptions with the objective of producing more comprehensible learning models. The central challenge is to…
Measurable Counterfactual Local Explanations for Any Classifier
Adam White, Artur d'Avila Garcez
We propose a novel method for explaining the predictions of any classifier. In our approach, local explanations are expected to explain both the outcome of a prediction and how tha…
Efficient predicate invention using shared "NeMuS"
Edjard Mota, Jacob M. Howe, Ana Schramm +1
Amao is a cognitive agent framework that tackles the invention of predicates with a different strategy as compared to recent advances in Inductive Logic Programming (ILP) approache…