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 …

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 …

Despite strong empirical performance for image classification, deep neural networks are
often regarded as``black boxes''and they are difficult to interpret. On the other hand, sparse …

Federated learning (FL) is an effective way to incorporate information provided by different
clients when a single local client is unable to provide sufficient training samples for …

A significant robustness gap exists between machine intelligence and human perception
despite recent advances in deep learning. Deep learning is not provably secure. A critical …

Several recent results provide theoretical insights into the phenomena of adversarial
examples. Existing results, however, are often limited due to a gap between the simplicity of …

Sparse auto-encoders are useful for extracting low-dimensional representations from high-
dimensional data. However, their performance degrades sharply when the input noise at test …

Despite their unmatched performance, deep neural networks remain susceptible to targeted
attacks by nearly imperceptible levels of adversarial noise. While the underlying cause of …

This work studies the adversarial robustness of parametric functions composed of a linear
predictor and a nonlinear representation map. Our analysis relies on sparse local …

曲波变换去噪处理使同相轴在断层等不连续区域发生畸变, 对有效信号产生干扰.
基于过完备字典信号稀疏表示(K-SVD) 需要人工反复调整参数才能改善去噪效果. 为此, 将K …