Quantifying the value of positive transfer: An experimental case study
University of Sheffield（谢菲尔德大学）；Politecnico di Torino（都灵理工大学）
Aidan J. Hughes, Giulia Delo, Jack Poole, Nikolaos Dervilis, Keith Worden
备注：
In Proceedings of the 11th European Workshop on Structural Health Monitoring (EWSHM 2024), Potsdam, Germany. July 2024. arXiv admin note: text overlap with arXiv:2311.03083
In traditional approaches to structural health monitoring, challenges often arise associated with the availability of labelled data. Population-based structural health monitoring seeks to overcomes these challenges by leveraging data/information from similar structures via technologies such as transfer learning. The current paper demonstrate a methodology for quantifying the value of information transfer in the context of operation and maintenance decision-making. This demonstration, based on a population of laboratory-scale aircraft models, highlights the steps required to evaluate the expected value of information transfer including similarity assessment and prediction of transfer efficacy. Once evaluated for a given population, the value of information transfer can be used to optimise transfer-learning strategies for newly-acquired target domains.
Simple Control Baselines for Evaluating Transfer Learning
Swiss Federal Institute of Technology (EPFL)（瑞士联邦理工学院（EPFL））；Moscow Institute of Physics Technology (MIPT)（莫斯科物理技术学院（MIPT））；transfer-controls.epfl.ch（transfer-controls.epfl.ch（网址，非机构））
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk
JC School of Public Health and Primary Care, The Chinese University of Hong Kong（香港中文大学赛马会公共卫生及基层医疗学院）；Hong Kong Polytechnic University（香港理工大学）
Precise High-Dimensional Asymptotics for Quantifying Heterogeneous Transfers
Yau Mathematical Sciences Center, Tsinghua University（清华大学数学科学中心）；Khoury College of Computer Sciences, Northeastern University（东北大学计算机科学学院）；Department of Computer Science, Stanford University（斯坦福大学计算机科学系）；Department of Statistics and Data Science, University of Pennsylvania（宾夕法尼亚大学统计与数据科学系）
Quantifying Hyperparameter Transfer and the Importance of Embedding Layer Learning Rate
Department of Physics, University of Maryland, College Park（马里兰大学物理系）；Department of Computer Science, University of Maryland, College Park（马里兰大学计算机科学系）；Joint Quantum Institute, University of Maryland, College Park（马里兰大学联合量子研究所）；Meta Superintelligence Labs, Fundamental AI Research（Meta超智能实验室，基础人工智能研究）
