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 …

The application of deep reinforcement learning (DRL) to computer and networked systems
has recently gained significant popularity. However, the obscurity of decisions by DRL …

Coconut milk is a soft target for adulterators owing to its simplicity of chemical composition.
Professionals and consumers want to control the originalitas of coconut milk, while sellers …

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) …

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

The use of neural networks in embedded systems is becoming increasingly common, but
these systems often operate in safety–critical environments, where a failure or incorrect …

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 …

In recent years, Deep Reinforcement Learning (DRL) has emerged as an effective approach
to solving real-world tasks. However, despite their successes, DRL-based policies suffer …

Deep Neural Networks are increasingly adopted in critical tasks that require a high level of
safety, eg, autonomous driving. While state-of-the-art verifiers can be employed to check …