"Functions"的相关文档 - 文库宝

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标签“Functions”的相关文档，共40条

Tilting the playing field Dynamical loss Functions for machine learning
Tiltingtheplayingﬁeld:DynamicallossFunctionsformachinelearningMiguelRuiz-Garcia12GeZhang1SamuelS.Schoenholz3AndreaJ.Liu1Abstracttionscale(Glorot&Bengio,2010;Xiaoetal.,2018)orlearningrateschedule(H...
the Functions Loss Dynamical Field
2023-11-16 19:42:0811171.23 MB21
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Posterior Value Functions Hindsight Baselines for Policy Gradient Methods
PosteriorValueFunctions:HindsightBaselinesforPolicyGradientMethodsChrisNota1BrunoCastrodaSilva1PhilipS.Thomas1Abstractcases,suchinformationcanbeusefulforassessingwhichoutcomeswerelikelytohaveoccurr...
for Policy Value Functions Posterior
2023-11-16 19:28:301954802.41 KB23
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Phase Transitions, Distance Functions, and Implicit Neural Representations
PhaseTransitions,DistanceFunctions,andImplicitNeuralRepresentationsYaronLipman12AbstractFigure1.ImplicitneuralrepresentationslearnedwiththenovelPHASEloss.Thisnewlossuniﬁesoccupancyandsigneddistanc...
Neural and Phase Functions Implicit
2023-11-16 19:28:2917159.08 MB21
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Optimizing persistent homology based Functions
OptimizingpersistenthomologybasedFunctionsMathieuCarrie`re1Fre´de´ricChazal2MarcGlisse2YuichiIke3HariprasadKannan2YuheiUmeda3Abstractthelastfewyears,agrowingnumberofworkshavesuc-cessfullybeenusin...
Persistent Homology Functions Based Optimizing
2023-11-16 19:28:261627528.47 KB4
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Mediated Uncoupled Learning Learning Functions without Direct Input-output Correspondences
MediatedUncoupledLearning:LearningFunctionswithoutDirectInput-outputCorrespondencesIkkoYamane12JunyaHonda32FlorianYger12MasashiSugiyama24Abstracttionpredictingsentimentofanimage(Mittaletal.,2018).E...
Learning without Functions Direct Mediated
2023-11-16 19:05:1518045.49 MB21
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Asymmetric Loss Functions for Learning with Noisy Labels
AsymmetricLossFunctionsforLearningwithNoisyLabelsXiongZhou12XianmingLiu12JunjunJiang12XinGao32XiangyangJi4AbstractToalleviatetheimpactoflabelnoisetoclassiﬁerlearning,onepopularresearchlineistodesi...
Learning for with Asymmetric Functions
2023-11-16 18:00:176835 MB24
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A Functional Perspective on Learning Symmetric Functions with Neural Networks
AFunctionalPerspectiveonLearningSymmetricFunctionswithNeuralNetworksAaronZweig1JoanBruna12AbstractSuchsymmetricFunctionsappearnaturallyacrossseveraldomains,includingparticlephysics,computergraphics...
Learning with on Functions Symmetric
2023-11-16 17:52:021476547.22 KB10
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Super-efficiency of automatic differentiation for Functions defined as a minimum
Super-efﬁciencyofautomaticdifferentiationforFunctionsdeﬁnedasaminimumPierreAblin1GabrielPeyré1ThomasMoreau2Abstractsparsecode,andListheLassocost(Mairaletal.,2010).Inthiscase,measurestheabilityof...
of for Automatic Functions differentiation
2023-11-14 21:46:381415775.68 KB5
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StochasticRank Global Optimization of Scale-Free Discrete Functions
StochasticRank:GlobalOptimizationofScale-FreeDiscreteFunctionsAlekseiUstimenko1LiudmilaProkhorenkova123AbstractTodealwiththediscretestructureofarankingloss,onecanusesomesmoothapproximation,whichise...
of Optimization Functions Discrete Global
2023-11-14 21:46:341257323.49 KB28
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Set Functions for Time Series
SetFunctionsforTimeSeriesMaxHorn12MichaelMoor12ChristianBock12BastianRieck12KarstenBorgwardt12Abstractacrossmodalitiestobealigned/synchronized,preventingtheirapplicationtotheaforementionedsettings....
for Time Series Set Functions
2023-11-14 21:46:23950505.05 KB6
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Peer Loss Functions Learning from Noisy Labels without Knowing Noise Rates
PeerLossFunctions:LearningfromNoisyLabelswithoutKnowingNoiseRatesYangLiu1HongyiGuo2Abstracttensivepriorworksintheliteraturethataimtodevelopal-gorithmstolearnmodelsthatarerobusttolabelnoise(By-Learn...
Learning from Functions Loss Labels
2023-11-14 21:45:4912581.28 MB10
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Optimization from Structured Samples for Coverage Functions
OptimizationfromStructuredSamplesforCoverageFunctionsWeiChen1XiaomingSun23JialinZhang23ZhijieZhang23Abstracteters.Thecomputationaltaskistooptimizeanobjectivefunctiongivenparametersofthemodel.Onesuc...
for from Optimization Functions Structured
2023-11-14 21:45:441951283.81 KB14
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On Second-Order Group Influence Functions for Black-Box Predictions
OnSecond-OrderGroupInﬂuenceFunctionsforBlack-BoxPredictionsSamyadeepBasu1XuchenYou1SoheilFeizi1Abstracttotracebackmodelpredictionstoitstrainingdata.Morespeciﬁcally,onecanaskwhichtrainingsampleswe...
for on Group Functions Influence
2023-11-14 21:45:311860635.03 KB30
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Normalized Loss Functions for Deep Learning with Noisy Labels
NormalizedLossFunctionsforDeepLearningwithNoisyLabelsXingjunMa1HanxunHuang1YisenWang2SimoneRomanoSarahErfani1JamesBailey1Abstract2018;Hanetal.,2018b);and4)robustlossFunctionsthatareinherentlytolera...
Learning for with Deep Functions
2023-11-14 21:45:2511685.31 MB9
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Interpreting Robust Optimization via Adversarial Influence Functions
InterpretingRobustOptimizationviaAdversarialInﬂuenceFunctionsZhunDeng1CynthiaDwork1JialiangWang1LinjunZhang2AbstracttionforamodelMisformulatedasRobustoptimizationhasbeenwidelyusedinminEx,ymaxl(θM...
Optimization Adversarial via Robust Functions
2023-11-14 21:44:43830343.58 KB17
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Fast and Private Submodular and $k$-Submodular Functions Maximization with Matroid Constraints
FastandPrivateSubmodularandk-SubmodularFunctionsMaximizationwithMatroidConstraintsAkbarRaﬁey1YuichiYoshida2AbstractcombinatorialoptimizationproblemsincludingtheMaxi-mumCoverage,MaximumCut,andFacil...
Submodular Maximization and Fast Functions
2023-11-14 21:44:10629309.81 KB1
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Efficiently sampling Functions from Gaussian process posteriors
EfﬁcientlySamplingFunctionsfromGaussianProcessPosteriorsJamesT.Wilson1ViacheslavBorovitskiy23AlexanderTerenin1PeterMostowsky2MarcPeterDeisenroth4Abstracttionandlong-termversusshort-termrewards.Bay...
from Sampling Gaussian Process Functions
2023-11-14 21:44:001771718.01 KB27
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Composable Sketches for Functions of Frequencies Beyond the Worst Case
ComposableSketchesforFunctionsofFrequencies:BeyondtheWorstCaseEdithCohen12OﬁrGeri34RasmusPagh156Abstractdatawithsmallcomputationalresources(time,communi-cation,andspace).Suchsketchessupportprocess...
of for Beyond Functions Sketches
2023-11-14 21:43:301149546.38 KB28
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Bandits for BMO Functions
BanditsforBMOFunctionsTianyuWang1CynthiaRudin1Abstracttheparameterspace,andtheymaycompletelymisstheoptima.Westudythebanditproblemwheretheunderly-ingexpectedrewardisaBoundedMeanOscilla-Asanotherexam...
for Functions Bandits BMO
2023-11-14 21:43:11644957.47 KB6
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Validating Causal Inference Models via Influence Functions
ValidatingCausalInferenceModelsviaInﬂuenceFunctionsAhmedM.Alaa1MihaelavanderSchaar123AbstractFigure1.Pictorialrepresentationofourvalidationprocedure.Toestimatetheperformancesoftwocompetingmodels(m...
Inference Models via Causal Functions
2023-11-13 14:48:5618181.03 MB27
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