"Empirical"的相关文档

Smooth $p$-Wasserstein Distance Structure, Empirical Approximation, and Statistical Applications
Smoothp-WassersteinDistance:Structure,EmpiricalApproximation,andStatisticalApplicationsSloanNietert1ZivGoldfeld2KengoKato3AbstractapplicationsinstatisticsandML,rangingfromgenerativemodeling(Arjovsk...
and Approximation Structure Smooth Empirical
2023-11-16 19:41:5214553.87 MB10
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Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models
EmpiricalStudyoftheBeneﬁtsofOverparameterizationinLearningLatentVariableModelsRares-DariusBuhai1YoniHalpern2YoonKim3AndrejRisteski4DavidSontag1Abstractgoalisfrequentlytoconﬁrmorrejectwhetherapart...
of the in Empirical Study
2023-11-14 21:44:021960901.81 KB9
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Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models
EmpiricalAnalysisofBeamSearchPerformanceDegradationinNeuralSequenceModelsEldanCohen1J.ChristopherBeck1Abstractinferencealgorithmfordecodingneuralsequencemodelsisbeamsearch,asearchalgorithmthatgener...
of Analysis Search Performance Empirical
2023-11-13 14:47:031705833.89 KB7
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Differentially Private Empirical Risk Minimization with Non-convex Loss Functions
DifferentiallyPrivateEmpiricalRiskMinimizationwithNon-convexLossFunctionsDiWang1ChangyouChen1JinhuiXu1Abstractsultsobtainedfromsensitivedatasothatthedistributionoftheperturbedresultsisinsensitiveto...
with Differentially Private Non-convex Minimization
2023-11-13 14:46:52514201.08 KB20
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Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization
InformationTheoreticGuaranteesforEmpiricalRiskMinimizationwithApplicationstoModelSelectionandLarge-ScaleOptimizationIbrahimAlabdulmohsin1AbstractadomainZ,andalossfunctionontheproductspacef:Inthispa...
for Guarantees Minimization Information Risk
2023-11-13 11:59:481558591.71 KB29
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Smooth $p$-Wasserstein Distance Structure, Empirical Approximation, and Statistical Applications

Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models

Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models

Differentially Private Empirical Risk Minimization with Non-convex Loss Functions

Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization