101 citations · 249 across the 7 of their papers we have counts for
3 papers · 1 filter
Approximate Latent Force Model Inference
Jacob D. Moss, Felix L. Opolka, Bianca Dumitrascu +1
Physically-inspired latent force models offer an interpretable alternative to purely data driven tools for inference in dynamical systems. They carry the structure of differential…
Logic Explained Networks
Gabriele Ciravegna, Pietro Barbiero, Francesco Giannini +4
The large and still increasing popularity of deep learning clashes with a major limit of neural network architectures, that consists in their lack of capability in providing human-…
Entropy-based Logic Explanations of Neural Networks
Pietro Barbiero, Gabriele Ciravegna, Francesco Giannini +3
Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have…