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标签“the”的相关文档，共346条

Joining datasets via data augmentation in the label space for neural networks
JoiningdatasetsviadataaugmentationinthelabelspaceforneuralnetworksJakeZhao(Junbo)1MingfengOu23LinjiXue2YunkaiCui2SaiWu1GangChen1AbstractInstead,itsversatilitycanbesubstantiallyenhancedbyanovelframe...
the via Data in Datasets
2023-11-16 18:47:0518311.98 MB22
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Improved Confidence Bounds for the Linear Logistic Model and Applications to Bandits
ImprovedConﬁdenceBoundsfortheLinearLogisticModelandApplicationstoBanditsKwang-SungJun1LalitJain2BlakeMason3HoussamNassif4Abstractafeaturevectorforeacharm.Acommonassumptionisthattherewardisanoisyli...
for the bounds Linear Improved
2023-11-16 18:47:00537460.77 KB9
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HyperHyperNetwork for the Design of Antenna Arrays
HyperHyperNetworksfortheDesignofAntennaArraysShaharLutati1LiorWolf12AbstractDesigningantennasisachallenginginverseproblem:whilemappingthestructureoftheantennatoitsradiationprop-Wepresentdeeplearnin...
of for the Design HyperHyperNetwork
2023-11-16 18:46:5816431.73 MB11
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How Important is the Train-Validation Split in Meta-Learning
HowImportantistheTrain-ValidationSplitinMeta-Learning?YuBai1MinshuoChen2PanZhou1TuoZhao2JasonD.Lee3ShamKakade4HuanWang1CaimingXiong1Abstract1.IntroductionMeta-learningaimstoperformfastadaptationMet...
the in How is Important
2023-11-16 18:46:571374780.83 KB28
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Heterogeneity for the Win One-Shot Federated Clustering
HeterogeneityfortheWin:One-ShotFederatedClusteringDonKurianDennis1TianLi1VirginiaSmith1AbstractInthecaseoffederatedlearning,clusteringhasfoundap-plicationsinclient-selection(Choetal.,2020),personal...
for the Clustering One-Shot Federated
2023-11-16 18:46:538302.14 MB7
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Guarantees for Tuning the Step Size using a Learning-to-Learn Approach
GuaranteesforTuningtheStepSizeusingaLearning-to-LearnApproachXiangWang1ShuaiYuan1ChenweiWu1RongGe1Abstractrinetal.(2015)consideredtheideaoftuningtheseparame-tersbyoptimization—thatis,considerameta...
for Using Guarantees the Tuning
2023-11-16 18:46:52717372.01 KB23
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Geometry of the Loss Landscape in Overparameterized Neural Networks Symmetries and Invariances
GeometryoftheLossLandscapeinOverparameterizedNeuralNetworks:SymmetriesandInvariancesBerﬁnS¸ims¸ek12Franc¸oisGed1ArthurJacot1FrancescoSpadaro1Cle´mentHongler1WulframGerstner2JohanniBrea2Abstrac...
of the in Geometry Loss
2023-11-16 18:46:4719121.46 MB25
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Generative Video Transformer Can Objects be the Words
GenerativeVideoTransformer:CanObjectsbetheWords?Yi-FuWu1JaesikYoon12SungjinAhn13Abstractinteresttodevelopananalogousgenerativepre-trainingpro-cedureforvideos,thecomputationaloverheadindealingTransf...
Generative the Can Be Video
2023-11-16 18:46:469003.08 MB16
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Generalization Bounds in the Presence of Outliers a Median-of-Means Study
GeneralizationBoundsinthePresenceofOutliers:aMedian-of-MeansStudyPierreLaforgue1GuillaumeStaerman2StephanClémençon2Abstractcorruptionoftheobservationsacquired.Fromastatisticalperspective,twoframe...
of the in generalization bounds
2023-11-16 18:46:4410285.42 MB13
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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
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Evaluating the Implicit Midpoint Integrator for Riemannian Hamiltonian Monte Carlo
EvaluatingtheImplicitMidpointIntegratorforRiemannianManifoldHamiltonianMonteCarloJamesA.Brofos1RoyR.Lederman1AbstractFigure1.Trajectoriescomputedbytheleapfrogintegratorandtheimplicitmidpointintegra...
for the Evaluating Implicit Riemannian
2023-11-16 18:38:02987610.28 KB7
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Equivariant message passing for the prediction of tensorial properties and molecular spectra
EquivariantMessagePassingforthePredictionofTensorialPropertiesandMolecularSpectraKristofT.Schu¨tt12OliverT.Unke12MichaelGastegger13Abstractoryquicklybecomeprohibitiveforlargesystemsandthepredictio...
of for the Prediction Message
2023-11-16 18:38:016961.09 MB8
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Disentangling syntax and semantics in the brain with deep networks
DisentanglingSyntaxandSemanticsintheBrainwithDeepNetworksCharlotteCaucheteux12AlexandreGramfort1Jean-RemiKing23AbstractA.B.theactivationsoflanguagetransformerslikeLexicalCompositionalGPT-2havebeens...
and the in Semantics Disentangling
2023-11-16 18:31:1118173.8 MB30
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Dimensionality Reduction for the Sum-of-Distances Metric
DimensionalityReductionforSum-of-DistancesMetricZhiliFeng1PraneethKacham1DavidP.Woodruff1Abstractsubspaceandperformthetaskonthelow-dimensionalpoints.GivenasetofnpointsA={a1,...,an}inRd,Wegiveadimen...
for the Reduction Dimensionality Metric
2023-11-16 18:31:0815713.04 MB18
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Differentially Private Aggregation in the Shuffle Model Almost Central Accuracy in Almost a Single Message
DifferentiallyPrivateAggregationintheShufﬂeModel:AlmostCentralAccuracyinAlmostaSingleMessageBadihGhazi1RaviKumar1PasinManurangsi1RasmusPagh21AmerSinha3Abstractwithextensionsincluding(private)distr...
Aggregation the in Differentially Private
2023-11-16 18:31:061787673.02 KB13
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DFAC Framework Factorizing the Value Function via Quantile Mixture for Multi-Agent Distributional Q-Learning
DFACFramework:FactorizingtheValueFunctionviaQuantileMixtureforMulti-AgentDistributionalQ-LearningWei-FangSun123Cheng-KuangLee2Chun-YiLee1Abstractoptimizetheoverallrewardsineachepisode.Nevertheless,...
the via Value Function Framework
2023-11-16 18:31:0413471.27 MB13
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Detection of Signal in the Spiked Rectangular Models
DetectionofSignalintheSpikedRectangularModelsJiHyungJung1HyeWonChung2JiOonLee13Abstractv∈RNwithu=v=1.theparameterλcorrespondstothesignal-to-noiseratio(SNR).Weconsidertheproblemofdetectingsignalsi...
of the in Detection Signal
2023-11-16 18:31:04893469.61 KB26
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Dense for the Price of Sparse Improved Performance of Sparsely Initialized Networks via a Subspace Offset
DenseforthePriceofSparse:ImprovedPerformanceofSparselyInitializedNetworksviaaSubspaceOffsetIlanPrice12JaredTanner12Abstract1.1.CompetingPrioritiesforSparseNetworksThatneuralnetworksmaybeprunedtohig...
of for Sparse the Improved
2023-11-16 18:31:038851.86 MB22
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Decentralized Riemannian Gradient Descent on the Stiefel Manifold
DecentralizedRiemannianGradientDescentontheStiefelManifoldShixiangChen1AlfredoGarcia1MingyiHong2ShahinShahrampour1AbstractagentoptimizationproblemWeconsiderdistributednon-convexoptimization1nwherea...
Gradient Descent on the Decentralized
2023-11-16 18:30:585493.86 MB2
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Crystallization Learning with the Delaunay Triangulation
CrystallizationLearningwiththeDelaunayTriangulationJiaqiGu1GuoshengYin1AbstractandE(2i)<∞.Nonparametricregressionisacollectionofmethodsforestimatingtheconditionalexpectationfunc-BasedontheDelaunay...
Learning with the Crystallization Delaunay
2023-11-16 18:30:541655940.73 KB12
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