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Hung-yiLifeHung-yiLifeLonghttps:///lifelong-learning-will-help-workers-navigate-future-能不能每次作業都訓練同一個類神經網路呢LifeLongLearningContinuousLearning,NeverEndingLearning,LifeLongLearningContinuousLearning,NeverEndingLearning,IncrementalIcansolvetask1.IcansolvetasksIcansolve…Task3Task2Task1Life-longModelLife-longModelbutNOT3layers,neurons=Example–3layers,neurons=Example–ThisisThisis明明可以把Task12Task1明明可以把Task12Task1Task2Example–QuestionExample–Question•Givenadocument,answerthequestionbasedon••Thereare20QAtasksinbAbiTrainaQAmodelthroughthe20Example–QuestionSequentiallytrainthe20Jointlytrainingthe20Example–QuestionSequentiallytrainthe20Jointlytrainingthe20是不為非不能WaitaminuteMulti-tasktrainingcansolveWaitaminuteMulti-tasktrainingcansolvetheUsingallthedataforforTaskAlwayskeeptheMulti-tasktrainingcanbeconsideredastheupperboundofLLL.Task1ElasticWeightConsolidationBasicIdea:Someparametersinthemodelareimportanttotheprevioustasks.Onlychangetheunimportantparameters.ElasticWeightConsolidationBasicIdea:Someparametersinthemodelareimportanttotheprevioustasks.Onlychangetheunimportantparameters.isthemodellearnedfromthepreviousEachparameter𝜃𝑏hasa“guard”HowthisparameterLossforcurrent2𝜃−=𝐿𝜃+𝜆frompreviousLosstoParameterstoElasticWeightConsolidationBasicIdea:Someparametersinthemodelareimportanttotheprevioustasks.OnlychangetheunimportantElasticWeightConsolidationBasicIdea:Someparametersinthemodelareimportanttotheprevioustasks.Onlychangetheunimportantparameters.isthemodellearnedfromthepreviousEachparameter𝜃𝑏hasa“guard”Onekindofregularization.𝜃𝑖shouldbeclosetocertain′=+If𝑏𝑖=0,thereisnoconstraintonIf𝑏𝑖=∞,𝜃𝑖wouldalwaysbeequalto2෍ 𝜃− ElasticWeightConsolidationElasticWeightConsolidationTheerrorsurfacesoftasks1&(darker=smallerElasticWeightConsolidation1Small2nd𝑏1isElasticWeightConsolidation1Small2nd𝑏1is2EachparameterhasLarge2nd𝑏2islargeElasticWeightConsolidationElasticWeightConsolidation𝑏1issmall,whileis𝜃1DonotElasticWeightElasticWeightConsolidationMNISTpermutation,fromtheoriginalEWCElasticWeightConsolidation•ElasticWeightConsolidation•ElasticWeightConsolidationSynapticIntelligenceMemoryAwareSynapses••••Specialpart:DonotneedlabelledSynapsis:Generating•Conductingmulti-tasklearningbyGenerating•Conductingmulti-tasklearningbygeneratingpseudo-usinggenerativetask1task1&2SolvetaskSolvetaskAddingNew•LearningAddingNew•Learningwithoutforgetting•iCaRL:IncrementalClassifierandRepresentationLife-longbutLife-longbutNOTModelWaitaminuteTrainaWaitaminuteTrainamodelforeachEventuallywecannotstoreallthemodelsTask3Task2Task1Life-Longv.s.IcandotaskLife-Longv.s.Icandotask2becauseIhavelearnedtask1.(Wedon’tcaremachinecanstilldotaskEventhoughIhavelearnedtask2,IdonotforgettaskLife-longTask2Task1𝑅𝑖,𝑗:afterontaskjIf𝑖>Aftertrainingtaski,doestaskjbeforgotIf𝑖<Canwetransfertheskilloftaskitotask1Accuracy𝑅𝑖,𝑗:afterontaskjIf𝑖>Aftertrainingtaski,doestaskjbeforgotIf𝑖<Canwetransfertheskilloftaskitotask1Accuracy1σ𝑇−1−(ItisusuallyRand…𝑅𝑖,𝑗:afterontaskjIf𝑖>Aftertrainingtaski,doestaskjbeforgotIf𝑖<Canwetransfertheskilloftaskitotask1Accuracy1σ𝑇−1−𝑅𝑖,𝑗:afterontaskjIf𝑖>Aftertrainingtaski,doestaskjbeforgotIf𝑖<Canwetransfertheskilloftaskitotask1Accuracy1σ𝑇−1−1−Rand…GEM:A-GEM:GradientEpisodicMemory•ConstraintthegradienttoGEM:A-GEM:GradientEpisodicMemory•Constraintthegradienttoimprovethepreviousasclose𝑔′∙𝑔1≥𝑔′∙𝑔2≥:negativegradientcurrentprevioustask:updateNeedthedatafrompreviousLife-longbutLife-longbutNOTModelProgressiveNeuralProgressiveNeuralExpertExpertAddsmal

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