Modern practice for training classification deepnets involves a terminal phase of training
(TPT), which begins at the epoch where training error first vanishes. During TPT, the training …

Deep neural networks provide unprecedented performance gains in many real-world
problems in signal and image processing. Despite these gains, the future development and …

Learning to optimize (L2O) is an emerging approach that leverages machine learning to
develop optimization methods, aiming at reducing the laborious iterations of hand …

Deep learning (DL) has had unprecedented success and is now entering scientific
computing with full force. However, current DL methods typically suffer from instability, even …

Intelligent fault diagnosis (IFD) has experienced tremendous progress owing to a great deal
to deep learning (DL)-based methods over the decades. However, the “black box” nature of …

Artificial neural network (ANN) has achieved great success in mechanical fault diagnosis
and has been widely used. However, traditional ANN is still opaque in terms of …

The present paper studies so-called deep image prior (DIP) techniques in the context of ill-
posed inverse problems. DIP networks have been recently introduced for applications in …

The sparse prior has been widely adopted to establish data models for numerous
applications. In this context, most of them are based on one of three foundational paradigms …

In mechanical anomaly detection, algorithms with higher accuracy, such as those based on
artificial neural networks, are frequently constructed as black boxes, resulting in opaque …

Deep convolutional neural networks (DCNNs) have recently emerged as a powerful tool to
deliver breakthrough performances in various image analysis and processing applications …