Deep reinforcement learning (DRL) has proven capable of superhuman performance on
many complex tasks. To achieve this success, DRL algorithms train a decision-making agent …

Today, many industrial processes are undergoing digital transformation, which often
requires the integration of well-understood domain models and state-of-the-art machine …

With the rapid growth of machine learning, deep neural networks (DNNs) are now being
used in numerous domains. Unfortunately, DNNs are “black-boxes”, and cannot be …

As deep reinforcement learning (RL) showcases its strengths in networking, its pitfalls are
also coming to the public's attention. Training on a wide range of network environments …

In this work, for the first time, we demonstrate that computers can automatically learn from
observing the heuristic efforts of the last four decades, stand on the shoulders of the existing …

Deep neural networks (DNNs) have gained significant popularity in recent years, becoming
the state of the art in a variety of domains. In particular, deep reinforcement learning (DRL) …

Marabou 2.0: A Versatile Formal Analyzer of Neural Networks | SpringerLink Skip to main
content Advertisement SpringerLink Account Menu Find a journal Publish with us Track your …

Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for
tasks where complex policies are learned within reactive systems. Unfortunately, these …

Convolutional neural networks (CNNs) have achieved immense popularity in areas like
computer vision, image processing, speech proccessing, and many others. Unfortunately …

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …