"Power"的相关文档

标签“Power”的相关文档，共15条

The Power of Adaptivity for Stochastic Submodular Cover
ThePowerofAdaptivityforStochasticSubmodularCoverRohanGhuge1AnupamGupta2ViswanathNagarajan1Abstractsoleimanetal.,2015;Batenietal.,2018):herearetwoexamplesfromsensordeploymentandmedicaldiagnosis.Inth...
of for Submodular Stochastic the
2023-11-16 19:42:066232.32 MB10
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The Power of Log-Sum-Exp Sequential Density Ratio Matrix Estimation for Speed-Accuracy Optimization
ThePowerofLog-Sum-Exp:SequentialDensityRatioMatrixEstimationforSpeed-AccuracyOptimizationTaikiMiyagawa1AkinoriF.Ebihara1AbstractMorietal.,2018).Earlyclassiﬁcationoftimeseriesisamulti-objectiveopti...
of the Density Sequential Power
2023-11-16 19:42:066591.71 MB25
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Pareto GAN Extending the Representational Power of GANs to Heavy-Tailed Distributions
ParetoGAN:ExtendingtheRepresentationalPowerofGANstoHeavy-TailedDistributionsToddHuster1JeremyE.J.Cohen1ZinanLin2KevinChan3CharlesKamhoua3NandiLeslie4Cho-YuJasonChiang1VyasSekar2AbstractEvans&Erland...
of the GAN Power Pareto
2023-11-16 19:28:281656653.6 KB3
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On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise
OnthePowerofLocalizedPerceptronforLabel-OptimalLearningofHalfspaceswithAdversarialNoiseJieShen1Abstractthelearnermustpayforeachlabelitwishestoberevealed.Thegoalistodesignqueryingstrategiestoavoidle...
of for on the Power
2023-11-16 19:15:471397334.74 KB26
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On the Generalization Power of Overfitted Two-Layer Neural Tangent Kernel Models
OntheGeneralizationPowerofOverﬁttedTwo-LayerNeuralTangentKernelModelsPeizhongJu1XiaojunLin1NessB.Shroff2AbstractMuthukumaretal.,2019;Juetal.,2020),aninteresting“double-descent”phenomenonhasbeeno...
of on the generalization Power
2023-11-16 19:15:46978532.24 KB12
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Communication-Efficient Distributed SVD via Local Power Iterations
Communication-EfﬁcientDistributedSVDviaLocalPowerIterationsXiangLi1ShusenWang2KunChen1ZhihuaZhang1AbstractWheneithernordisbig,thedatamatrixA∈Rn×dmaynotﬁtinthememory,makingstandardsingle-machine...
Distributed Local via Communication-efficient Power
2023-11-16 18:11:231632643.25 KB14
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On the Power of Compressed Sensing with Generative Models
OnthePowerofCompressedSensingwithGenerativeModelsAkshayKamath1SushrutKarmalkar1EricPrice1Abstractofthecompressedrepresentationofxthantoitsambientdimensionn.Thegoalofcompressedsensingistolearnastruc...
of with on the Compressed
2023-11-14 21:45:341687381.33 KB28
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Power k-Means Clustering
Powerk-MeansClusteringJasonXu1KennethLange2Abstractforimplementingit(Steinhaus,1956;Lloyd,1982).Lloyd’salgorithmalternatesbetweentwostepsofmembershipre-Clusteringisafundamentaltaskinunsupervisedas...
Clustering k-Means Power
2023-11-13 14:48:16703599.14 KB20
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On The Power of Curriculum Learning in Training Deep Networks
OnThePowerofCurriculumLearninginTrainingDeepNetworksGuyHacohen12DaphnaWeinshall1Abstracttypicallyreﬂectstheircomplexity.Thestudentisthengrad-uallyintroducedtotheseconceptsbyincreasingcomplexity,Tr...
Learning of on Curriculum the
2023-11-13 14:48:051424602.8 KB11
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The Power of Interpolation Understanding the Effectiveness of SGD in Modern Over-parametrized Learning
ThePowerofInterpolation:UnderstandingtheEffectivenessofSGDinModernOver-parametrizedLearning†SiyuanMa1RaefBassily1MikhailBelkin1Abstract1IntroductionInthispaperweaimtoformallyexplainthephe-Mostmach...
of the the Power Understanding
2023-11-13 12:00:511418586.36 KB29
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Orthogonal Machine Learning Power and Limitations
OrthogonalMachineLearning:PowerandLimitationsLesterMackey1VasilisSyrgkanis1IliasZadik12Abstractinaccurateinferencesconcerningtheparametersofinterest(thetreatmenteffects).√Doublemachinelearningprov...
Learning and Machine Orthogonal Power
2023-11-13 12:00:2316021.15 MB30
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On the Power of Over-parametrization in Neural Networks with Quadratic Activation
OnthePowerofOver-parametrizationinNeuralNetworkswithQuadraticActivationSimonS.Du1JasonD.Lee2Abstractreasonwhylocalsearchalgorithmscanachievelowtrainingerror.Theintuitionisover-parametrizationWeprov...
of Neural on the in
2023-11-13 12:00:191656302.12 KB1
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Bounds on the Approximation Power of Feedforward Neural Networks
BoundsontheApproximationPowerofFeedforwardNeuralNetworksMohammadMehrabi1AslanTchamkerten2MansoorI.Youseﬁ2AbstractDeterminingthecapacityofaneuralnetworkswithapiece-wiselinearactivationfunctiontypic...
of Approximation on the bounds
2023-11-13 11:59:111490356.54 KB28
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Tensor Decomposition via Simultaneous Power Iteration
TensorDecompositionviaSimultaneousPowerIterationPo-AnWang1Chi-JenLu1Abstractandinfactseveralproblemsrelatedtotensordecomposi-tionareknowntobeNP-hard(Hillar&Lim,2013).Nev-Tensordecompositionisanimpo...
via Tensor Decomposition Power Iteration
2023-11-12 20:45:22784316.7 KB3
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On the Expressive Power of Deep Neural Networks
OntheExpressivePowerofDeepNeuralNetworksMaithraRaghu12BenPoole3JonKleinberg1SuryaGanguli3JaschaSohlDickstein2Abstractperformance.WeproposeanewapproachtotheproblemofIndeed,theveryﬁrstresultsonthisq...
of Neural on Deep the
2023-11-12 20:44:5617691.03 MB12
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