A perspective survey on deep transfer learning for fault diagnosis in industrial scenarios: Theories, applications and challenges

W Li, R Huang, J Li, Y Liao, Z Chen, G He… - … Systems and Signal …, 2022 - Elsevier
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 …

A comprehensive survey on poisoning attacks and countermeasures in machine learning

Z Tian, L Cui, J Liang, S Yu - ACM Computing Surveys, 2022 - dl.acm.org
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 …

Factors associated with healthy aging in Latin American populations

H Santamaria-Garcia, A Sainz-Ballesteros… - Nature medicine, 2023 - nature.com
Latin American populations may present patterns of sociodemographic, ethnic and cultural
diversity that can defy current universal models of healthy aging. The potential combination …

Ota: Optimal transport assignment for object detection

Z Ge, S Liu, Z Li, O Yoshie… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
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 …

Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data

J Huang, D Guan, A **ao, S Lu - Advances in neural …, 2021 - proceedings.neurips.cc
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 …

A forest fire detection system based on ensemble learning

R Xu, H Lin, K Lu, L Cao, Y Liu - Forests, 2021 - mdpi.com
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 …

Toward explainable artificial intelligence for precision pathology

F Klauschen, J Dippel, P Keyl… - Annual Review of …, 2024 - annualreviews.org
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 …

On hyperparameter optimization of machine learning algorithms: Theory and practice

L Yang, A Shami - Neurocomputing, 2020 - Elsevier
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 …

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 …

Review of deep learning: concepts, CNN architectures, challenges, applications, future directions

L Alzubaidi, J Zhang, AJ Humaidi, A Al-Dujaili… - Journal of big Data, 2021 - Springer
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 …