Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

Rule extraction (RE) from recurrent neural networks (RNNs) refers to finding models of the
underlying RNN, typically in the form of finite state machines, that mimic the network to a …

The COVID-19 pandemic continues to have major impact to health and medical
infrastructure, economy, and agriculture. Prominent computational and mathematical models …

Social scientists and psychologists take interest in understanding how people express
emotions and sentiments when dealing with catastrophic events such as natural disasters …

This book sets out to build bridges between the domains of photonic device physics and
neural networks, providing a comprehensive overview of the emerging field of" …

Autonomous robots must be able to learn and maintain models of their environments.
Research on mobile robot navigation has produced two major paradigms for mapping …

Recently, fully connected recurrent neural networks have been proven to be computationally
rich-at least as powerful as Turing machines. This work focuses on another network which is …

A structured organization of information is typically required by symbolic processing. On the
other hand, most connectionist models assume that data are organized according to …

To date, the preponderance of techniques for eliciting the knowledge embedded in trained
artificial neural networks (ANN's) has focused primarily on extracting rule-based …