activity
20162023
most citedNeural-Symbolic Learning and Reasoning: A Survey and Interpretation

241 citations · 442 across the 13 of their papers we have counts for

collaborators
Showing 2019Show all

5 papers · 1 filter

cs.LG20195 cited

Making Good on LSTMs' Unfulfilled Promise

Daniel Philps, Artur d'Avila Garcez, Tillman Weyde

LSTMs promise much to financial time-series analysis, temporal and cross-sectional inference, but we find that they do not deliver in a real-world financial management task. We exa…

cs.AI2019

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…

cs.AI2019

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…

cs.AI201999 cited

Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning

Artur d'Avila Garcez, Marco Gori, Luis C. Lamb +3

Current advances in Artificial Intelligence and machine learning in general, and deep learning in particular have reached unprecedented impact not only across research communities,…

cs.LG20191 cited

Continual Learning Augmented Investment Decisions

Daniel Philps, Tillman Weyde, Artur d'Avila Garcez +1

Investment decisions can benefit from incorporating an accumulated knowledge of the past to drive future decision making. We introduce Continual Learning Augmentation (CLA) which i…