相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。Self-Supervised Learning: Generative or Contrastive
Xiao Liu et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2023)
A Systematic Review on Data Scarcity Problem in Deep Learning: Solution and Applications
Aayushi Bansal et al.
ACM COMPUTING SURVEYS (2022)
Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions
Ameya D. Jagtap et al.
NEUROCOMPUTING (2022)
Self-supervised pretraining via contrast learning for intelligent incipient fault detection of bearings
Yifei Ding et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2022)
Uncertainty quantification of granular computing-neural network model for prediction of pollutant longitudinal dispersion coefficient in aquatic streams
Behzad Ghiasi et al.
SCIENTIFIC REPORTS (2022)
Audacity of huge: overcoming challenges of data scarcity and data quality for machine learning in computational materials discovery
Aditya Nandy et al.
CURRENT OPINION IN CHEMICAL ENGINEERING (2022)
Self-supervised learning methods and applications in medical imaging analysis: a survey
Saeed Shurrab et al.
PEERJ COMPUTER SCIENCE (2022)
A robust model selection framework for fault detection and system health monitoring with limited failure examples: Heterogeneous data fusion and formal sensitivity bounds
Roberto Rocchetta et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2022)
A Survey of Deep Active Learning
Pengzhen Ren et al.
ACM COMPUTING SURVEYS (2022)
Novel virtual sample generation using conditional GAN for developing soft sensor with small data
Qun-Xiong Zhu et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2021)
Predictive maintenance enabled by machine learning: Use cases and challenges in the automotive industry
Andreas Theissler et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2021)
A Survey on Contrastive Self-Supervised Learning
Ashish Jaiswal et al.
TECHNOLOGIES (2021)
Recurrent Neural Networks for Time Series Forecasting: Current status and future directions
Hansika Hewamalage et al.
INTERNATIONAL JOURNAL OF FORECASTING (2021)
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Ameya D. Jagtap et al.
JOURNAL OF COMPUTATIONAL PHYSICS (2020)
Potential, challenges and future directions for deep learning in prognostics and health management applications
Olga Fink et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)
Towards multi-model approaches to predictive maintenance: A systematic literature survey on diagnostics and prognostics
Juan Jose Montero Jimenez et al.
JOURNAL OF MANUFACTURING SYSTEMS (2020)
Improving maritime traffic emission estimations on missing data with CRBMs
Alberto Gutierrez-Torre et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2020)
Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture
Andre Listou Ellefsen et al.
RELIABILITY ENGINEERING & SYSTEM SAFETY (2019)
Deep Transfer Learning Based on Sparse Autoencoder for Remaining Useful Life Prediction of Tool in Manufacturing
Chuang Sun et al.
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS (2019)
Data scarcity in modelling and simulation of a large-scale WWTP: Stop sign or a challenge
Sina Borzooei et al.
JOURNAL OF WATER PROCESS ENGINEERING (2019)
Bearing remaining useful life prediction based on deep autoencoder and deep neural networks
Lei Ren et al.
JOURNAL OF MANUFACTURING SYSTEMS (2018)
Recent Developments in Deep Learning for Engineering Applications
Athanasios Voulodimos et al.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE (2018)
Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Autoencoder with Multilayer Self-Learning
Jian Ma et al.
COMPLEXITY (2018)
Residual Recurrent Neural Networks for Learning Sequential Representations
Boxuan Yue et al.
INFORMATION (2018)
Prognostics and Health Management: A Review on Data Driven Approaches
Kwok L. Tsui et al.
MATHEMATICAL PROBLEMS IN ENGINEERING (2015)
Prediction intervals to account for uncertainties in neural network predictions: Methodology and application in bus travel time prediction
Ehsan Mazloumi et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE (2011)
Exploiting mobility for energy efficient data collection in wireless sensor networks
S Jain et al.
MOBILE NETWORKS & APPLICATIONS (2006)