The primary objective of this collection is to showcase cutting-edge research on explainability techniques and applications of deep learning neural networks, encompassing a spectrum of neural ...

Today, neural networks have already solved the challenges ... Neurosymbolic AI is therefore also related to the notion of explainability. Rather than simply trusting that an algorithmic output ...

Five common ML models (logistic regression, support vector machine (SVM), neural networks, decision trees (DT), and random forest (RF)) were applied. The proposed explainability methods (HEX-SC ...

Researchers applied the mathematical theory of synchronization to clarify how recurrent neural networks (RNNs) generate predictions, revealing a certain map, based on the generalized synchronization, ...

A collaborative team of researchers from Carnegie Mellon University and the University of Pittsburgh designed a clever experiment using a brain-controlled interface to determine whether one-way ...

Integration of explainability and the human-in-the-loop model can ... This type of AI algorithm is not currently common since ...