A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges
Abstract Deep Transfer Learning (DTL) is a new paradigm of machine learning, which can
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
not only leverage the advantages of Deep Learning (DL) in feature representation, but also …
A comprehensive survey on poisoning attacks and countermeasures in machine learning
The prosperity of machine learning has been accompanied by increasing attacks on the
training process. Among them, poisoning attacks have become an emerging threat during …
training process. Among them, poisoning attacks have become an emerging threat during …
Factors associated with healthy aging in Latin American populations
Latin American populations may present patterns of sociodemographic, ethnic and cultural
diversity that can defy current universal models of healthy aging. The potential combination …
diversity that can defy current universal models of healthy aging. The potential combination …
Ota: Optimal transport assignment for object detection
Recent advances in label assignment in object detection mainly seek to independently
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …
define positive/negative training samples for each ground-truth (gt) object. In this paper, we …
Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …
target domain, but it requires to access the source data which often raises concerns in data …
A forest fire detection system based on ensemble learning
Due to the various shapes, textures, and colors of fires, forest fire detection is a challenging
task. The traditional image processing method relies heavily on manmade features, which is …
task. The traditional image processing method relies heavily on manmade features, which is …
Toward explainable artificial intelligence for precision pathology
The rapid development of precision medicine in recent years has started to challenge
diagnostic pathology with respect to its ability to analyze histological images and …
diagnostic pathology with respect to its ability to analyze histological images and …
On hyperparameter optimization of machine learning algorithms: Theory and practice
Abstract Machine learning algorithms have been used widely in various applications and
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
areas. To fit a machine learning model into different problems, its hyper-parameters must be …
SF-FWA: A Self-Adaptive Fast Fireworks Algorithm for effective large-scale optimization
M Chen, Y Tan - Swarm and Evolutionary Computation, 2023 - Elsevier
Computationally efficient algorithms for large-scale black-box optimization have become
increasingly important in recent years due to the growing complexity of engineering and …
increasingly important in recent years due to the growing complexity of engineering and …
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …