Anomaly detection of machine tools plays a vital role in the machinery industry to sustain
efficient operation and avoid catastrophic failures. Compared to traditional machine learning …

Modern convolutional networks are not shift-invariant, as small input shifts or translations
can cause drastic changes in the output. Commonly used downsampling methods, such as …

Cloud cover is a common and inevitable phenomenon that often hinders the usability of
optical remote sensing (RS) image data and further interferes with continuous cartography …

Graph pooling is a central component of a myriad of graph neural network (GNN)
architectures. As an inheritance from traditional CNNs, most approaches formulate graph …

Introduction to Machine Learning with Applications in Information Security, Second Edition
provides a classroom-tested introduction to a wide variety of machine learning and deep …

Thanks to the use of convolution and pooling layers, convolutional neural networks were for
a long time thought to be shift-invariant. However, recent works have shown that the output …

Effective user authentication is key to ensuring equipment security, data privacy, and
personalized services in Internet of Things (IoT) systems. However, conventional mode …

Distracted driving has become one of the main contributors to traffic accidents. It is therefore
of great interest for intelligent vehicles to establish a distraction detection system that can …

When designing a semantic segmentation model for a real-world application, such as
autonomous driving, it is crucial to understand the robustness of the network with respect to …

It is widely believed that the success of deep networks lies in their ability to learn a
meaningful representation of the features of the data. Yet, understanding when and how this …