Intra-domain and cross-domain transfer learning for time series data -- How transferable are the features?
University of Rijeka（里耶卡大学）；Istituto Nazionale di Geofisica e Vulcanologia（国家地球物理与火山研究所）；Roma Tre University（罗马第三大学）；Center for Artificial Intelligence and Cybersecurity, University of Rijeka（里耶卡大学人工智能与网络安全中心）
Erik Otović, Marko Njirjak, Dario Jozinović, Goran Mauša, Alberto Michelini, Ivan Štajduhar
发表信息：
Knowledge-Based Systems, Volume 239, 5 March 2022, 107976
In practice, it is very demanding and sometimes impossible to collect datasets of tagged data large enough to successfully train a machine learning model, and one possible solution to this problem is transfer learning. This study aims to assess how transferable are the features between different domains of time series data and under which conditions. The effects of transfer learning are observed in terms of predictive performance of the models and their convergence rate during training. In our experiment, we use reduced data sets of 1,500 and 9,000 data instances to mimic real world conditions. Using the same scaled-down datasets, we trained two sets of machine learning models: those that were trained with transfer learning and those that were trained from scratch. Four machine learning models were used for the experiment. Transfer of knowledge was performed within the same domain of application (seismology), as well as between mutually different domains of application (seismology, speech, medicine, finance). We observe the predictive performance of the models and the convergence rate during the training. In order to confirm the validity of the obtained results, we repeated the experiments seven times and applied statistical tests to confirm the significance of the results. The general conclusion of our study is that transfer learning is very likely to either increase or not negatively affect the predictive performance of the model or its convergence rate. The collected data is analysed in more details to determine which source and target domains are compatible for transfer of knowledge. We also analyse the effect of target dataset size and the selection of model and its hyperparameters on the effects of transfer learning.
Noise May Contain Transferable Knowledge: Understanding Semi-supervised Heterogeneous Domain Adaptation from an Empirical Perspective
Beijing Teleinfo Technology Company Ltd.（北京 Teleinfo 科技有限公司）；China Academy of Information and Communications Technology（中国信息通信研究院）；Inspur Computer Technology Co., Ltd.（浪潮计算机技术有限公司）；Southern University of Science and Technology（南方科技大学）；Research Institute of Industrial Internet of Things（工业互联网物联网研究院）；Hong Kong University of Science and Technology（香港科技大学）；WeBank（微众银行）
TS-ACL: Closed-Form Solution for Time Series-oriented Continual Learning
Central South University（中南大学）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；Zhengzhou University（郑州大学）；The University of Hong Kong（香港大学）；Peking University（北京大学）；University of Maryland（马里兰大学）；South China University of Technology（华南理工大学）
Learning Transferable Policies from Action-free Time Series Through Dynamical Embeddings
Hector Institute for AI in Psychiatry (HITKIP)（赫克托人工智能精神病学研究所）；Central Institute of Mental Health (CIMH)（中央精神健康研究所）；Heidelberg University（海德堡大学）；Interdisciplinary Center for Scientific Computing (IWR)（跨学科科学计算中心）；Hertie Institute for AI in Brain Health（赫蒂人工智能脑健康研究所）；University of Tübingen（蒂宾根大学）
Self-explaining Hierarchical Model for Intraoperative Time Series
McKelvey School of Engineering, Washington University in St. Louis（圣路易斯华盛顿大学麦凯尔维工程学院）；School of Medicine, Washington University in St. Louis（圣路易斯华盛顿大学医学院）
Revitalizing Multivariate Time Series Forecasting: Learnable Decomposition with Inter-Series Dependencies and Intra-Series Variations Modeling
University of Electronic Science and Technology of China（电子科技大学）；The Hong Kong Polytechnic University（香港理工大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；University of Cambridge（剑桥大学）
Robust Multivariate Time Series Forecasting against Intra- and Inter-Series Transitional Shift
School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；Tongji University（同济大学）；School of Computer Science and Technology, Beijing Institute of Technology（北京理工大学计算机科学与技术学院）；Data Science Institute, University of Technology Sydney（悉尼科技大学数据科学研究所）；School of Computing, Macquarie University（麦考瑞大学计算学院）
Pre-training Epidemic Time Series Forecasters with Compartmental Prototypes
Emory University Department of Computer Science Atlanta GA USA（埃默里大学计算机科学系）；Emory University Rollins School of Public Health Atlanta GA USA（埃默里大学罗林斯公共卫生学院）；Emory University（埃默里大学）
Deep Coupling Network For Multivariate Time Series Forecasting
School of Computer Science, Beijing Institute of Technology（北京理工大学计算机学院）；School of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Medical Technology, Beijing Institute of Technology（北京理工大学医学技术学院）；Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院）；School of Computer Science and Information Engineering, HeFei University of Technology（合肥工业大学计算机与信息工程学院）
Towards Transferable Defense Against Malicious Image Edits
State Key Laboratory of AI Safety, Institute of Computing Technology, Chinese Academy of Sciences (CAS)（人工智能安全国家重点实验室，计算技术研究所，中国科学院）；University of China Academy of Sciences（中国科学院大学）；School of Computer Science, China University of Geosciences（中国地质大学（武汉）计算机学院）
Analyzing Poverty through Intra-Annual Time-Series: A Wavelet Transform Approach
Chalmers University of Technology（查尔姆斯理工大学）；Institute for Analytical Sociology, Linköping University（林雪平大学分析社会学研究所）；Center for Advanced Study in the Behavioral Sciences, Stanford University（斯坦福大学行为科学高级研究中心）；The AI and Global Development Lab（人工智能与全球发展实验室）
A Denoising VAE for Intracardiac Time Series in Ischemic Cardiomyopathy
Dept. of Computer Science ETH Zurich（计算机科学系，苏黎世联邦理工学院）；Dept. of Medicine Stanford University（医学系，斯坦福大学）；Dept. of Cardiology Amsterdam University（心脏病学系，阿姆斯特丹大学）
Bayesian Time-Series Classifier for Decoding Simple Visual Stimuli from Intracranial Neural Activity
Isfahan University of Technology（伊斯法罕理工大学）；Worcester Polytechnic Institute（伍斯特理工学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
Lightweight Time Series Data Valuation on Time Series Foundation Models via In-Context Finetuning
Sun Yat-sen University State Key Laboratory of Internet Architecture（中山大学互联网架构国家重点实验室）；Tsinghua University（清华大学）；National University of Singapore（新加坡国立大学）
STEP: Scientific Time-Series Encoder Pretraining via Cross-Domain Distillation
Shanghai Aritificial Intelligence Laboratory（上海人工智能实验室）；Tsinghua University（清华大学）；Harbin Institute of Technology（哈尔滨工业大学）；Sichuan University（四川大学）；University of Cambridge（剑桥大学）
