6 citations · 11 across the 9 of their papers we have counts for
3 papers · 1 filter
Accelerating NeurASP with vectorization and caching
Alexander Philipp Rader, Alessandra Russo
Neurosymbolic AI combines neural networks with symbolic programs to create robust and explainable predictions. One such framework is NeurASP, which trains a neural network to predi…
Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and Editable Policies
Kexin Gu Baugh, Luke Dickens, Alessandra Russo
Although deep reinforcement learning has been shown to be effective, the model's black-box nature presents barriers to direct policy interpretation. To address this problem, we pro…
Neuro-Symbolic Learning of Answer Set Programs from Raw Data
Daniel Cunnington, Mark Law, Jorge Lobo +1
One of the ultimate goals of Artificial Intelligence is to assist humans in complex decision making. A promising direction for achieving this goal is Neuro-Symbolic AI, which aims…