"at"的相关文档

标签“at”的相关文档，共11条

Regularized Submodular Maximization at Scale
RegularizedSubmodularMaximizationatScaleEhsanKazemi1ShervinMinaee2MoranFeldman3AminKarbasi4Abstractpromotescoverageandrepresentativeness(Weietal.,2015)(wecallthisanoptimizationperspective)orsamplin...
Submodular Maximization at Scale Regularized
2023-11-16 19:41:2916321.35 MB20
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Z-GCNETs Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
Z-GCNETs:TimeZigzagsatGraphConvolutionalNetworksforTimeSeriesForecastingYuzhouChen12IgnacioSegovia-Dominguez34YuliaR.Gel32Abstract1.IntroductionThererecentlyhasbeenasurgeofinterestinde-Manyrealworl...
Networks Graph Time at Convolutional
2023-11-16 19:41:291838910.25 KB22
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Megaverse Simulating Embodied Agents at One Million Experiences per Second
Megaverse:SimulatingEmbodiedAgentsatOneMillionExperiencesperSecondAlekseiPetrenko12ErikWijmans13BrennanShacklett4VladlenKoltun1Figure1.Megaverseisanewresearchplatformthatsupportssimulatingembodieda...
at Agents One Megaverse Simulating
2023-11-16 19:05:168194.17 MB28
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A Second look at Exponential and Cosine Step Sizes Simplicity, Adaptivity, and Performance
ASecondlookatExponentialandCosineStepSizes:Simplicity,Adaptivity,andPerformanceXiaoyuLi∗1ZhenxunZhuang∗2FrancescoOrabona123Abstracttypicallybetterscalewiththecomplexityofthepredic-torsandtheamoun...
and at Exponential Look Step
2023-11-16 17:51:5812821 MB16
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Poisson Learning Graph Based Semi-Supervised Learning at Very Low Label Rates
PoissonLearning:GraphBasedSemi-SupervisedLearningatVeryLowLabelRatesJeffCalder1BrendanCook1MatthewThorpe2DejanSlepcˇev3Abstractranking(Heetal.,2004;Yangetal.,2013;Xuetal.,2011),amongmanyotherprobl...
Learning Graph at Based Semi-Supervised
2023-11-14 21:45:5215241.1 MB11
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Understanding Priors in Bayesian Neural Networks at the Unit Level
UnderstandingPriorsinBayesianNeuralNetworksattheUnitLevelMariiaVladimirova12JakobVerbeek1PabloMesejo3JulyanArbel1AbstractworksonBayesianneuralnetworks(Neal,1992;MacKay,1992).Thereisalargevarietyofa...
Neural Networks Bayesian in at
2023-11-13 14:48:54648553.92 KB14
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Concentration Inequalities for Conditional Value at Risk
ConcentrationInequalitiesforConditionalValueatRiskPhilipS.Thomas1ErikLearned-Miller1AbstractPrashanth&Ghavamzadeh,2013;Chow&Ghavamzadeh,2014;Tamaretal.,2015;Pintoetal.,2017;Morimuraetal.,Inthispape...
for Conditional at Value Risk
2023-11-13 14:46:4314713.82 MB17
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Data Summarization at Scale A Two-Stage Submodular Approach
DataSummarizationatScale:atwo-StageSubmodularApproachMarkoMitrovic1EhsanKazemi1MortezaZadimoghaddam2AminKarbasi1AbstractdriverssuggestedwaitinglocationsacrossNewYorkCitybasedonhistoricalriderpick-u...
Submodular Data at Summarization Approach
2023-11-13 11:59:229102.85 MB25
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BOHB Robust and Efficient Hyperparameter Optimization at Scale
BOHB:RobustandEfﬁcientHyperparameterOptimizationatScaleStefanFalkner1AaronKlein1FrankHutter1Abstractforapracticalsolutiontothehyperparameteroptimization(HPO)problemthatfulﬁllsmanydesiderata:Moder...
Efficient Optimization and Robust at
2023-11-13 11:59:111466538.92 KB14
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A Closer Look at Memorization in Deep Networks
ACloserLookatMemorizationinDeepNetworksDevanshArpit12StanisławJastrze˛bski3NicolasBallas12DavidKrueger12EmmanuelBengio4MaxinderS.Kanwal5TeganMaharaj16AsjaFischer7AaronCourville128YoshuaBengio129S...
Networks Deep in at Closer
2023-11-12 20:45:3212633.09 MB12
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Collect at Once, Use Effectively Making Non-interactive Locally Private Learning Possible
CollectatOnce,UseEffectively:MakingNon-interactiveLocallyPrivateLearningPossibleKaiZheng1WenlongMou1LiweiWang1Abstractetal.,2016),machinelearning(Chaudhuri&Monteleoni,2008;Chaudhurietal.,2011;Rubin...
Collect at Once Use Effectively
2023-11-12 20:44:001937303.44 KB18
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