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标签“from”的相关文档，共138条

Discovering Context Effects from Raw Choice Data
DiscoveringContextEffectsfromRawChoiceDataArjunSeshadri1AlexanderPeysakhovich2JohanUgander1AbstractThemostwellknownandwidelyusedRUMisthecondi-tionalmultinomiallogit(MNL),alsocalledtheLucemodel,Many...
from Data Choice Discovering Effects
2023-11-13 14:46:541904837.69 KB10
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Classification from Positive, Unlabeled and Biased Negative Data
ClassiﬁcationfromPositive,UnlabeledandBiasedNegativeDataYu-GuanHsieh∗1GangNiu2MasashiSugiyama23Abstractbelongingtothisclassaretoodiversetobeexhaustivelyannotated.Thesameproblemarisesintextclassi...
from and Classification Unlabeled Positive
2023-11-13 14:46:3813641.23 MB17
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Adversarial examples from computational constraints
AdversarialExamplesfromComputationalConstraintsSe´bastienBubeck1YinTatLee12EricPrice3IlyaRazenshteyn1AbstractfromsomedistributionD,onewishestoapproximatelyminimizePX∼D(h(X)=f(X)).Amorechallenging...
from Adversarial Constraints Examples Computational
2023-11-13 14:46:211801225.48 KB27
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Active Learning for Decision-Making from Imbalanced Observational Data
ActiveLearningforDecision-MakingfromImbalancedObservationalDataIirisSundin1PeterSchulam2EeroSiivola1AkiVehtari1SuchiSaria2SamuelKaski1Abstractdecision,x.Then,thegoalistoestimatep(YX=x,A=a)and,furth...
Learning Active for from Observational
2023-11-13 14:46:171151602.96 KB15
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A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes
APolynomialTimeMCMCMethodforSamplingfromContinuousDeterminantalPointProcessesAlirezaRezaei1ShayanOveisGharan1Abstract(Kuleszaetal.,2012;Chaoetal.,2015;Yaoetal.,2016;Gillenwateretal.,2012),videosumm...
for from Sampling Time Method
2023-11-13 14:46:141423414.09 KB10
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Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings
TrainableCalibrationMeasuresForNeuralNetworksfromKernelMeanEmbeddingsAviralKumar1SunitaSarawagi1UjjwalJain1Abstractlearnedmodelsfeedintodecisionsystemsorarehumanin-terpreted.Alsocalibrationisuseful...
Neural for from Networks Calibration
2023-11-13 12:00:551447689.32 KB23
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Residual Unfairness in Fair Machine Learning from Prejudiced Data
ResidualUnfairnessinFairMachineLearningfromPrejudicedDataNathanKallus1AngelaZhou2Abstractnewquestionsaboutthepossibleharmsoflearningfromdatawhichissubjecttohistoricalbias.Unlikeclean-cutpre-Recentw...
Learning from in Machine Fair
2023-11-13 12:00:341366678.64 KB29
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Pseudo-task Augmentation from Deep Multitask Learning to Intratask Sharing—and Back
Pseudo-taskAugmentation:fromDeepMultitaskLearningtoIntrataskSharing—andBackElliotMeyerson12RistoMiikkulainen12Abstract2018).DeepMTLreliesontrainingsignalsfrommultipledatasetstotraindeepstructureth...
Learning from Deep to Multitask
2023-11-13 12:00:301738536.32 KB7
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Projection-Free Online Optimization with Stochastic Gradient from Convexity to Submodularity
Projection-FreeOnlineOptimizationwithStochasticGradient:fromConvexitytoSubmodularityLinChen12ChristopherHarshaw13HamedHassani4AminKarbasi12Abstract1.IntroductionOnlineoptimizationhasbeenasuccessful...
from Online Optimization with Gradient
2023-11-13 12:00:281778538.9 KB12
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PDE-Net Learning PDEs from Data
PDE-Net:LearningPDEsfromDataZichaoLong∗1YipingLu∗1XianzhongMa∗12BinDong345Abstract1.IntroductionPartialdifferentialequations(PDEs)playapromi-Differentialequations,especiallypartialdifferentialeq...
Learning from Data PDE-Net PDEs
2023-11-13 12:00:259132.33 MB23
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On Learning Sparsely Used Dictionaries from Incomplete Samples
OnLearningSparselyUsedDictionariesfromIncompleteSamplesThanhV.Nguyen1AkshaySoni2ChinmayHegde1Abstractcentresults(Spielmanetal.,2012;Agarwaletal.,2013;2014;Aroraetal.,2014;2015;Sunetal.,2015;Chatter...
Learning from on Incomplete Sparsely
2023-11-13 12:00:181020468.99 KB17
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Neural Program Synthesis from Diverse Demonstration Videos
NeuralProgramSynthesisfromDiverseDemonstrationVideosShao-HuaSun1HyeonwooNoh2SriramSomasundaram1JosephJ.Lim1AbstractDemonstrationsInterpretingdecisionmakinglogicindemonstra-demo1SynthesizedProgramti...
Neural from Synthesis Program Diverse
2023-11-13 12:00:1516373.49 MB9
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Learning Registered Point Processes from Idiosyncratic Observations
LearningRegisteredPointProcessesfromIdiosyncraticObservationsHongtengXu12LawrenceCarin1HongyuanZha3AbstractRegisteredIntensityfunctionTemporalAparametricpointprocessmodelisdeveloped,withmodelingbas...
Learning from Processes Point Registered
2023-11-13 11:59:596912.53 MB15
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Learning Localized Spatio-Temporal Models from Streaming Data
LearningLocalizedSpatio-TemporalModelsfromStreamingDataMuhammadOsama1DaveZachariah1ThomasScho¨n1AbstractsWeaddresstheproblemofpredictingspatio-ttemporalprocesseswithtemporalpatternsthatvaryacrosss...
Learning from Models Data Localized
2023-11-13 11:59:56982972.36 KB20
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Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
LearningHiddenMarkovModelsfromPairwiseCo-occurrenceswithApplicationtoTopicModelingKejunHuang1XiaoFu2NicholasD.Sidiropoulos3Abstract...Xt−1XtXt+1...WepresentanewalgorithmforidentifyingtheYt−1YtYt+...
Learning from Models Hidden Markov
2023-11-13 11:59:551844313.14 KB21
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Learn from Your Neighbor Learning Multi-modal Mappings from Sparse Annotations
LearnfromYourNeighbor:LearningMulti-modalMappingsfromSparseAnnotationsAshwinKalyan1StefanLee1AnithaKannan2DhruvBatra13AbstractAhomekitchenwithwoodencabinetsandadinningtable.Manystructuredprediction...
Learning from Learn Mappings neighbor
2023-11-13 11:59:53818693.2 KB26
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Gradient Coding from Cyclic MDS Codes and Expander Graphs
GradientCodingfromCyclicMDSCodesandExpanderGraphsNetanelRaviv1ItzhakTamo2RashishTandon3AlexandrosG.Dimakis4Abstractdistributedsynchronousgradientdescent.Gradientcodingisatechniqueforstragglermit-Th...
from and Gradient Coding Codes
2023-11-13 11:59:401432910.73 KB3
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Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples
ExtractingAutomatafromRecurrentNeuralNetworksUsingQueriesandCounterexamplesGailWeiss1YoavGoldberg2EranYahav1AbstractExactLearningIntheﬁeldofexactlearning,concepts(setsofinstances)canbelearnedpreci...
Neural from Using Networks Recurrent
2023-11-13 11:59:321786283.89 KB28
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Decoupling Gradient-Like Learning Rules from Representations
DecouplingGradient-LikeLearningRulesfromRepresentationsPhilipS.Thomas1ChristophDann2EmmaBrunskill3Abstractworkpoorlywith,orbeincompatiblewith,others.Althoughthisintertwiningisunavoidableandislikely...
Learning from Representations Decoupling Gradient-Like
2023-11-13 11:59:231463947.17 KB24
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Classification from Pairwise Similarity and Unlabeled Data
ClassiﬁcationfromPairwiseSimilarityandUnlabeledDataHanBao12GangNiu2MasashiSugiyama21AbstractTable1:Explanationsofclassiﬁcationandclustering.Supervisedlearningneedsahugeamountofla-ProblemExplanati...
from and Data Classification Unlabeled
2023-11-13 11:59:146981.66 MB23
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