5 papers
Mixture of Concept Bottleneck Experts
Francesco De Santis, Gabriele Ciravegna, Giovanni De Felice +7
Concept Bottleneck Models (CBMs) promote interpretability by grounding predictions in human-understandable concepts. However, existing CBMs typically constrain their task predictor…
DeepProofLog: Efficient Proving in Deep Stochastic Logic Programs
Ying Jiao, Rodrigo Castellano Ontiveros, Luc De Raedt +4
Neurosymbolic (NeSy) AI aims to combine the strengths of neural architectures and symbolic reasoning to improve the accuracy, interpretability, and generalization capability of AI…
Grounding Methods for Neural-Symbolic AI
Rodrigo Castellano Ontiveros, Francesco Giannini, Marco Gori +2
A large class of Neural-Symbolic (NeSy) methods employs a machine learner to process the input entities, while relying on a reasoner based on First-Order Logic to represent and pro…
Neural Interpretable Reasoning
Pietro Barbiero, Giuseppe Marra, Gabriele Ciravegna +5
We formalize a novel modeling framework for achieving interpretability in deep learning, anchored in the principle of inference equivariance. While the direct verification of inter…
Relational Reasoning Networks
Giuseppe Marra, Michelangelo Diligenti, Francesco Giannini
Neuro-symbolic methods integrate neural architectures, knowledge representation and reasoning. However, they have been struggling at both dealing with the intrinsic uncertainty of…