Adaptive Learning Architectures for Evolving Data Streams: Challenges and Advances
Keywords:
Adaptive Learning Systems, Evolving Data Streams, Lifelong Learning, Knowledge Retention, Meta-Learning, Online Learning ArchitecturesAbstract
In the era of ubiquitous data generation, learning systems must continuously adapt to dynamic and non-stationary environments. This paper surveys recent advances in adaptive learning architectures designed for evolving data streams, with a focus on incremental learning, concept drift adaptation, and real-time model updating. We analyze the core challenges in building robust, scalable systems capable of retaining long-term knowledge while remaining flexible to new information. Key architectural designs, such as modular networks, meta-learning frameworks, and memory- constrained learners, are discussed. Real-world applications in IoT, autonomous systems, and personalized services are examined to highlight practical implications. This work aims to provide a comprehensive understanding of the current landscape and identify open research directions in the field of lifelong and adaptive learning.
References
[1]C.Aljundi,F.Babiloni,M.Elhoseiny,M.Rohrbach,andT.Tuytelaars,"Memoryawaresynapses:Learningwhat(not)toforget,"inProceedingsoftheEuropeanConferenceonComputerVision(ECCV),2018,pp.139-154.[2]R.Kemker,M.McClure,A.Abitino,T.Hayes,andC.Kanan,"Measuringcatastrophicforgettinginneuralnetworks,"inProceedingsoftheAAAIConferenceonArtificialIntelligence,vol.32,no.1,2018.[3]J.Gama,I.Žliobaitė,A.Bifet,M.Pechenizkiy,andA.Bouchachia,"Asurveyonconceptdriftadaptation,"ACMComputingSurveys,vol.46,no.4,pp.1-37,2014.[4]Y.Wang,Q.Yao,J.T.Kwok,andL.M.Ni,"Generalizingfromafewexamples:Asurveyonfew-shotlearning,"ACMComputingSurveys,vol.53,no.3,pp.1-34,2020.[5]S.ThrunandT.M.Mitchell,"Lifelongrobotlearning,"RoboticsandAutonomousSystems,vol.15,no.1-2,pp.25-46,1995.[6]T.Diethe,A.Borchert,M.Girolami,andN.Lawrence,"Continuallearninginpractice,"inProceedingsoftheNeurIPS2019WorkshoponContinualLearning,2019.[7]M.DeLangeetal.,“Acontinuallearningsurvey:Defyingforgettinginclassificationtasks,”IEEETransactionsonPatternAnalysisandMachineIntelligence,vol.44,no.7,pp.3366-3385,2021.[8]D.Wang,Z.Yan,andJ.Li,"Acomprehensivesurveyofcontinuallearning:Theory,methodandapplication,"Neurocomputing,vol.489,pp.249-270,2022.[9]H.HeandE.A.Garcia,"Learningfromimbalanceddata,"IEEETransactionsonKnowledgeandDataEngineering,vol.21,no.9,pp.1263-1284,2009.[10]I.Žliobaitė,"Learningunderconceptdrift:Anoverview,"2010.arXiv:1010.4784.[11]J.Kirkpatricketal.,"Overcomingcatastrophicforgettinginneuralnetworks,"ProceedingsoftheNationalAcademyofSciences,vol.114,no.13,pp.3521-3526,2017.[12]F.Zenke,B.Poole,andS.Ganguli,"Continuallearningthroughsynapticintelligence,"inProceedingsofthe34thInternationalConferenceonMachineLearning(ICML),2017,pp.3987-3995.[13]D.Lopez-PazandM.Ranzato,"Gradientepisodicmemoryforcontinuallearning,"inAdvancesinNeuralInformationProcessingSystems(NeurIPS),2017,pp.6467-6476.[14]R.Rolnicketal.,"Experiencereplayforcontinuallearning,"inAdvancesinNeuralInformationProcessingSystems(NeurIPS)Workshop,2019.[15]A.Rusuetal.,"Progressiveneuralnetworks,"2016.arXiv:1606.04671.[16]A.MallyaandS.Lazebnik,"PackNet:Addingmultipletaskstoasinglenetworkbyiterativepruning,"inProceedingsoftheIEEEConferenceonComputerVisionandPatternRecognition(CVPR),2018,pp.7765-7773.[17]R.Polikar,L.Udpa,S.Udpa,andV.Honavar,"Learn++:Anincrementallearningalgorithmforsupervisedneuralnetworks,"IEEETransactionsonSystems,Man,andCybernetics,PartC,vol.31,no.4,pp.497-508,2001.[18]H.M.Gomesetal.,"Adaptiverandomforestsforevolvingdatastreamclassification,"MachineLearning,vol.106,no.9,pp.1469-1495,2017.[19]C.Finn,P.Abbeel,andS.Levine,"Model-agnosticmeta-learningforfastadaptationofdeepnetworks,"inProceedingsofthe34thInternationalConferenceonMachineLearning(ICML),2017,pp.1126-1135.[20]J.Snell,K.Swersky,andR.Zemel,"Prototypicalnetworksforfew-shotlearning,"inAdvancesinNeuralInformationProcessingSystems(NeurIPS),2017,pp.4077-4087.[21]M.Masanaetal.,"Class-incrementallearning:surveyandperformanceevaluation,"2020.arXiv:2010.15277.[22]H.Zhang,M.Cisse,Y.Dauphin,andD.Lopez-Paz,“Few-shotopen-setrecognitionusingmeta-learning,”inProceedingsoftheIEEEConferenceonComputerVisionandPatternRecognition(CVPR),2020,pp.8798-8807.[23]M.Mundt,C.Hong,I.Pliushch,andV.Ramesh,“Awholisticviewofcontinuallearningwithdeepneuralnetworks:Forgottenlessonsandthefuture,”Neurocomputing,vol.438,pp.230-246,2021.
11/11MartinC.E.etal./AILS,Vol.[24]Y.Chen,X.Yao,andM.Zhang,“Federatedlifelonglearningviadynamictaskroutingandsharedmemory,”inProceedingsofthe38thAAAIConferenceonArtificialIntelligence,2024.[25]A.Borsos,A.Mishra,andT.Hofmann,“Classimbalanceandlong-tailincontinuallearning,”inAdvancesinNeuralInformationProcessingSystems(NeurIPS),2020,pp.14496-14508.
Downloads
Published
Issue
Section
License
Copyright (c) 2023 Future - Adaptive Intelligence and Lifelong Systems
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
