"low"的相关文档

标签“low”的相关文档，共18条

Single Pass Entrywise-Transformed low Rank Approximation
SinglePassEntrywise-TransformedlowRankApproximationYifeiJiang1YiLi2YimingSun2JiaxinWang3DavidP.Woodruff4Abstract1.IntroductionInapplicationssuchasnaturallanguageprocess-Therearenumerousapplications...
Rank Approximation low Single Pass
2023-11-16 19:41:49531324.33 KB11
下载文档
Finding the Stochastic Shortest Path with low Regret the Adversarial Cost and Unknown Transition Case
FindingtheStochasticShortestPathwithlowRegret:TheAdversarialCostandUnknownTransitionCaseLiyuChen1HaipengLuo1Abstractendwithinaﬁxednumberofstepsisextensivelystudiedinrecentyears(oftenknownasepisodi...
with Stochastic the low Path
2023-11-16 18:38:12797422.01 KB6
下载文档
Estimating $α$-Rank from A Few Entries with low Rank Matrix Completion
Estimating↵-RankfromAFewEntrieswithlowRankMatrixCompletionYaliDu1XueYan2XuChen3JunWang1HaifengZhang2Abstractetal.,2018;Omidshaﬁeietal.,2019).TheEloandTrueSkillbothdealwithratingsofagentsinthepair...
from with low Estimating few
2023-11-16 18:38:0211142.47 MB10
下载文档
Additive Error Guarantees for Weighted low Rank Approximation
AdditiveErrorGuaranteesforWeightedlowRankApproximationAdityaBhaskara1AravindaKanchanaRuwanpathirana1MaheshakyaWijewardena1Abstractmatrixversionturnsouttobechallenging.Formally,thelow-rankapproximat...
for Rank Guarantees low Error
2023-11-16 18:00:2515842.56 MB15
下载文档
Supervised Quantile Normalization for low Rank Matrix Factorization
SupervisedQuantileNormalizationforlow-rankMatrixApproximationMarcoCuturi1OlivierTeboul1JonathanNiles-Weed2Jean-PhilippeVert1Abstractmann,2001;Févotte&Idier,2011),binary(Slawskietal.,2013)orevendes...
for Rank Matrix low Supervised
2023-11-14 21:46:3913243.64 MB8
下载文档
On Efficient low Distortion Ultrametric Embedding
OnEfﬁcientlowDistortionUltrametricEmbeddingVincentCohen-Addad1KarthikC.S.2GuillaumeLagarde3Abstract1.IntroductionAclassicprobleminunsupervisedlearningandThecurseofdimensionalityhasruthlesslybeenha...
Efficient on low embedding Distortion
2023-11-14 21:45:281268281.08 KB20
下载文档
Multinomial Logit Bandit with low Switching Cost
MultinomialLogitBanditwithlowSwitchingCostKefanDong1YingkaiLi2QinZhang3YuanZhou4Abstractthatno-purchaseisthemostfrequentchoice,whichisverynaturalinretailing.W.l.o.g.,weassumev0=1,andvi1Westudymul...
with low Bandit Multinomial switching
2023-11-14 21:45:161390605.9 KB3
下载文档
low Bias low Variance Gradient Estimates for Hierarchical Boolean Stochastic Networks
lowBiaslowVarianceGradientEstimatesforHierarchicalBooleanStochasticNetworksAdeelPervez1TacoCohen2EfstratiosGavves1Abstractgradients,onecansamplegradientsfromsomedistribu-tion.Thesampleestimatescanb...
for Gradient Variance Hierarchical low
2023-11-14 21:45:051025738.96 KB5
下载文档
Learning Near Optimal Policies with low Inherent Bellman Error
LearningNearOptimalPolicieswithlowInherentBellmanErrorAndreaZanette1AlessandroLazaric2MykelKochenderfer1EmmaBrunskill1Abstract1.IntroductionWestudytheexplorationproblemwithapprox-Improvingthesample...
Learning with Optimal low Policies
2023-11-14 21:44:54538524.02 KB28
下载文档
Input-Sparsity low Rank Approximation in Schatten Norm
Input-SparsitylowRankApproximationinSchattenNormYiLi1DavidP.Woodruff2Abstractlarvaluedecomposition(SVD)ofA,whichisanexpensiveoperation.Wegivetheﬁrstinput-sparsitytimealgorithmsfortherank-klowranka...
Rank Approximation in low Norm
2023-11-14 21:44:41774321.62 KB17
下载文档
Adding seemingly uninformative labels helps in low data regimes
AddingSeeminglyUninformativeLabelsHelpsinlowDataRegimesChristosMatsoukas123AlbertBouIHernandez1YueLiu12KarinDembrower45GiseleMiranda12EmirKonuk12JohanFredinHaslum123AthanasiosZouzos6PeterLindholm4F...
in low Labels Adding seemingly
2023-11-14 21:43:007162.89 MB22
下载文档
SWALP Stochastic Weight Averaging in low Precision Training
SWALP:StochasticWeightAveraginginlow-PrecisionTrainingGuandaoYang1TianyiZhang1PolinaKirichenko1JunwenBai1AndrewGordonWilson1ChristopherDeSa1Abstractandaccumulategradientinformationinhigherprecision...
Precision Stochastic in low Weight
2023-11-13 14:48:421126790.03 KB6
下载文档
low Latency Privacy Preserving Inference
lowLatencyPrivacyPreservingInferenceAlonBrutzkus1OrenElisha2RanGilad-Bachrach3Abstractdictionservice,whichshouldbecapableofoperatingontheencrypteddatawithouthavingaccesstotherawdata.Whenapplyingmac...
Inference low Privacy Latency Preserving
2023-11-13 14:47:47635403.5 KB29
下载文档
Extreme Learning to Rank via low Rank Assumption
ExtremeLearningtoRankvialowRankAssumptionMinhaoCheng1IanDavidson1Cho-JuiHsieh12AbstractNegahbanetal.,2012;Wauthieretal.,2013).WeconsiderthesettingwherewewishtoperformHowever,inmanymodernapplication...
Learning Rank via low to
2023-11-13 11:59:3217941.02 MB17
下载文档
ZipML Training Linear Models with End-to-End low Precision, and a Little Bit of Deep Learning
ZipML:TrainingLinearModelswithEnd-to-EndlowPrecision,andaLittleBitofDeepLearningHantianZhang1JerryLi2KaanKara1DanAlistarh13JiLiu4CeZhang1AbstractMachineDataMovementChannelsSpeedupbecauseofLearningo...
Models with low End-to-End Linear
2023-11-12 20:45:311344829.44 KB4
下载文档
On Approximation Guarantees for Greedy low Rank Optimization
OnApproximationGuaranteesforGreedylowRankOptimizationRajivKhanna1EthanR.Elenberg1AlexandrosG.Dimakis1JoydeepGhosh1SahandNegahban2AbstractUnfortunately,theaboveoptimizationcanbecomputation-allytaxin...
for Rank Approximation Guarantees on
2023-11-12 20:44:5415961.5 MB26
下载文档
No Spurious Local Minima in Nonconvex low Rank Problems A Unified Geometric Analysis
NoSpuriousLocalMinimainNonconvexlowRankProblems:AUniﬁedGeometricAnalysisRongGe1ChiJin2YiZheng1Abstract2016;Parketal.,2016)andmatrixcompletion(Geetal.,2016)havewell-behavedoptimizationlandscape:all...
Local in low No Spurious
2023-11-12 20:44:541001361.93 KB22
下载文档
Contextual Decision Processes with low Bellman rank are PAC-Learnable
ContextualDecisionProcesseswithlowBellmanrankarePAC-LearnableNanJiang1AkshayKrishnamurthy2AlekhAgarwal3JohnLangford3RobertE.Schapire3AbstracteralizeMDPswherethestateformsthecontext(Ex.1)andPOMDPswh...
Rank with Contextual Decision Processes
2023-11-12 20:44:041462361.92 KB15
下载文档

首页上页 1 下页尾页