This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

This report summarizes the second International Verification of Neural Networks
Competition (VNN-COMP 2021), held as a part of the 4th Workshop on Formal Methods for …

Deep Learning (DL) has gained wide attention in recent years. Meanwhile, bugs in DL
systems can lead to serious consequences, and may even threaten human lives. As a result …

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 …

Deep neural networks have emerged as a widely used and effective means for tackling
complex, real-world problems. However, a major obstacle in applying them to safety-critical …

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) …

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 …

Deep neural networks (DNNs) are increasingly being used as controllers in reactive
systems. However, DNNs are highly opaque, which renders it difficult to explain and justify …