99 citations · 187 across the 23 of their papers we have counts for
5 papers · 1 filter
Logical Boltzmann Machines
Son N. Tran, Artur d'Avila Garcez
The idea of representing symbolic knowledge in connectionist systems has been a long-standing endeavour which has attracted much attention recently with the objective of combining…
CogniFNN: A Fuzzy Neural Network Framework for Cognitive Word Embedding Evaluation
Xinping Liu, Zehong Cao, Son Tran
Word embeddings can reflect the semantic representations, and the embedding qualities can be comprehensively evaluated with human natural reading-related cognitive data sources. In…
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,…
Unsupervised Neural-Symbolic Integration
Son N. Tran
Symbolic has been long considered as a language of human intelligence while neural networks have advantages of robust computation and dealing with noisy data. The integration of ne…
Propositional Knowledge Representation and Reasoning in Restricted Boltzmann Machines
Son N. Tran
While knowledge representation and reasoning are considered the keys for human-level artificial intelligence, connectionist networks have been shown successful in a broad range of…