"in"的相关文档

标签“in”的相关文档，共437条

Learning in POMDPs with Monte Carlo Tree Search
LearninginPOMDPswithMonteCarloTreeSearchSammieKatt1FransA.Oliehoek2ChristopherAmato1Abstracttodecisionmakingbymaintainingaprobabilitydistribu-tionoverpossiblemodelsastheagentactsinanonlinerein-TheP...
Learning with in Tree Monte
2023-11-12 20:44:401520478.37 KB28
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Improving Stochastic Policy Gradients in Continuous Control with Deep Reinforcement Learning using the Beta Distribution
ImprovingStochasticPolicyGradientsinContinuousControlwithDeepReinforcementLearningusingtheBetaDistributionPo-WeiChou1DanielMaturana1SebastianScherer1AbstractFigure1.Anexampleofcontinuouscontrolwith...
Stochastic in Policy Improving Gradients
2023-11-12 20:44:3216491.2 MB2
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Identification and Model Testing in Linear Structural Equation Models using Auxiliary Variables
IdentiﬁcationandModelTestinginLinearStructuralEquationModelsusingAuxiliaryVariablesBryantChen1DanielKumor2EliasBareinboim2Abstracteffectisidentiﬁedrequiresmodelingtheunderlyingcausalstructure,whi...
Structural Identification and Model in
2023-11-12 20:44:311465444.14 KB1
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Identify the Nash Equilibrium in Static Games with Random Payoffs
IdentifytheNashEquilibriuminStaticGameswithRandomPayoffsYichiZhou1JialianLi1JunZhu1Abstracttoestimatethepracticalgamesthroughsimulation.intheempiricalmodeling,pure-strategyproﬁlesofplayersareWestu...
the in Games Equilibrium Identify
2023-11-12 20:44:311612343.65 KB30
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Gradient Coding Avoiding Stragglers in Distributed Learning
GradientCoding:AvoidingStragglersinDistributedLearningRashishTandon1QiLei2AlexandrosG.Dimakis3NikosKarampatziakis4AbstractW1W2W3WeproposeanovelcodingtheoreticframeworkD1D2D3formitigatingstragglersi...
Learning Distributed Gradient in Avoiding
2023-11-12 20:44:2817083.11 MB27
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Generalization and Equilibrium in Generative Adversarial Nets (GANs)
GeneralizationandEquilibriuminGenerativeAdversarialNets(GANs)SanjeevArora1RongGe2YingyuLiang1TengyuMa1YiZhang1AbstractFigure1.ProbabilitydensityDrealwithmanypeaksandvalleysItisshownthattrainingofge...
Adversarial and Generative in generalization
2023-11-12 20:44:267901.74 MB9
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Follow the Moving Leader in Deep Learning
FollowtheMovingLeaderinDeepLearningShuaiZheng1JamesT.Kwok1Abstractever,trainingdeepnetworksisdifﬁcultastheoptimiza-tioncansufferfrompathologicalcurvatureandgetstuckDeepnetworksarehighlynonlinearan...
Learning Deep the in Follow
2023-11-12 20:44:2412971023.67 KB3
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Fairness in Reinforcement Learning
FairnessinReinforcementLearning⇤ShahinJabbariMatthewJosephMichaelKearnsJamieMorgensternAaronRoth1Abstracttingswherehistoricalcontextcanhaveadistinctinﬂuenceonthefuture.Forconcreteness,weconsidert...
Learning Reinforcement in Fairness
2023-11-12 20:44:221927530.97 KB21
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Efficient Regret Minimization in Non-Convex Games
EfﬁcientRegretMinimizationinNon-ConvexGamesEladHazan1KaranSingh1CyrilZhang1Abstractinthispaperweinvestigatethegeneralizationofthenon-convexstatistical,orbatch,learningmodeltoonlinelearn-Weconsider...
Efficient in Non-convex Regret Minimization
2023-11-12 20:44:19804464.12 KB3
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Dropout inference in Bayesian Neural Networks with Alpha-divergences
DropoutinferenceinBayesianNeuralNetworkswithAlpha-divergencesYingzhenLi1YarinGal12Abstractinformationcanbeused,forexample,toidentifywhenavi-sionmodelisgivenanadversarialimage(studiedbelow),Toobtain...
Neural Networks Inference with Bayesian
2023-11-12 20:44:1611761.05 MB14
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Differentially Private Submodular Maximization Data Summarization in Disguise
DifferentiallyPrivateSubmodularMaximization:DataSummarizationinDisguiseMarkoMitrovic1MarkBun12AndreasKrause3AminKarbasi1AbstractKirchhoff&Bilmes,2014;Siposetal.,2012),crowdteach-ing(Singlaetal.,201...
Submodular Maximization Data in Summarization
2023-11-12 20:44:149314.35 MB22
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Differentially Private Clustering in High-Dimensional Euclidean Spaces
DifferentiallyPrivateClusteringinHigh-DimensionalEuclideanSpacesMaria-FlorinaBalcan1TravisDick1YingyuLiang2WenlongMou3HongyangZhang1Abstractthequalityofmanydifferentareasofprivatedataanalysis.Westu...
High-dimensional in Clustering Differentially Private
2023-11-12 20:44:141636320.67 KB16
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Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
Depth-WidthTradeoffsinApproximatingNaturalFunctionswithNeuralNetworksItaySafran1OhadShamir1Abstractcentempiricalevidencesuggeststhatstandardfeedforwarddeepnetworksarehardertooptimizethanshallowerne...
with in Natural Depth-Width Tradeoffs
2023-11-12 20:44:121960420.26 KB2
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Deciding How to Decide Dynamic Routing in Artificial Neural Networks
DecidingHowtoDecide:DynamicRoutinginArtiﬁcialNeuralNetworksMasonMcGill1PietroPerona1AbstractMilner,1992),andfacesandotherbehaviorally-relevantstimuliellicitresponsesinanatomicallydistinct,special-...
in Dynamic to Deciding How
2023-11-12 20:44:0710601.88 MB12
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DARLA Improving Zero-Shot Transfer in Reinforcement Learning
DARLA:ImprovingZero-ShotTransferinReinforcementLearning111111IrinaHigginsArkaPalAndreiRusuLoicMattheyChristopherBurgessAlexanderPritzel111MatthewBotvinickCharlesBlundellAlexanderLerchnerAbstractef...
Learning Reinforcement in DARLA Improving
2023-11-12 20:44:0718124.56 MB28
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ChoiceRank Identifying Preferences from Node Traffic in Networks
ChoiceRank:IdentifyingPreferencesfromNodeTrafﬁcinNetworksLucasMaystre1MatthiasGrossglauser1Abstractitgetsfrompageslinkingtoit).BuildinguponrecentworkbyKumaretal.(2015),wepresentastatisticalframewo...
from in ChoiceRank Identifying Preferences
2023-11-12 20:43:581651244.23 KB24
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Being Robust (in High Dimensions) Can Be Practical
BeingRobust(inHighDimensions)CanBePracticalIliasDiakonikolas1GautamKamath2DanielM.Kane3JerryLi2AnkurMoitra2AlistairStewart1Abstractgivencomefromanicedistribution,butthatanadversaryhasthepowertoarbi...
Being Robust in High Dimensions
2023-11-12 20:43:561090703.85 KB27
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