"Are"的相关文档

标签“Are”的相关文档，共19条

What Are Bayesian Neural Network Posteriors Really Like
WhatAreBayesianNeuralNetworkPosteriorsReallyLike?PavelIzmailov1SharadVikram2MatthewD.Hoffman2AndrewGordonWilson1Abstractforneuralnetworkspromisesimprovedpredictions,reli-ableuncertaintyestimates,an...
Neural Bayesian Network Are Posteriors
2023-11-16 19:42:261194469.07 KB30
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Two Heads Are Better Than One Hypergraph-Enhanced Graph Reasoning for Visual Event Ratiocination
TwoHeadsAreBetterThanOne:Hypergraph-EnhancedGraphReasoningforVisualEventRatiocinationWenboZheng12LanYan23ChaoGou4Fei-YueWang2Abstract1.IntroductionEvenwithastillimage,humanscanratiocinateInaclassro...
Are Better Two than One
2023-11-16 19:42:156502.79 MB4
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Not All Memories Are Created Equal Learning to Forget by Expiring
NotAllMemoriesAreCreatedEqual:LearningtoForgetbyExpiringSainbayarSukhbaatar1DaJu1SpencerPoff1StephenRoller1ArthurSzlam1JasonWeston1AngelaFan12AbstractSukhbaatAretal.,2019a).However,acriticalcompone...
Learning Are All Not Created
2023-11-16 19:15:3910481.54 MB20
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Linear Transformers Are Secretly Fast Weight Programmers
LinearTransformersAreSecretlyFastWeightProgrammersImanolSchlag∗1KazukiIrie∗1Ju¨rgenSchmidhuber1Abstractﬁeldnetwork(Ramsaueretal.,2021;Krotov&Hopﬁeld,2016;Demircigiletal.,2017).Itextendsaformof...
Fast Are Linear Weight Transformers
2023-11-16 19:05:089921.27 MB10
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Evaluating Robustness of Predictive Uncertainty Estimation Are Dirichlet-based Models Reliable
EvaluatingRobustnessofPredictiveUncertaintyEstimation:AreDirichlet-basedModelsReliable?Anna-KathrinKopetzki1BertrandCharpentier1DanielZügner1SandhyaGiri1StephanGünnemann1AbstractFigure1.Visualiza...
of Estimation Evaluating Are Robustness
2023-11-16 18:38:021884710.4 KB7
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Cumulants of Hawkes Processes Are Robust to Observation Noise
CumulantsofHawkesProcessesAreRobusttoObservationNoiseWilliamTrouleau1JalalEtesami2MatthiasGrossglauser1NegarKiyavash2PatrickThiran1AbstracttheyAreusedtomodelthestochastictimeevolutionoflimitorderbo...
of Robust Processes to Are
2023-11-16 18:30:5417681.82 MB20
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Transformers Are RNNs Fast Autoregressive Transformers with Linear Attention
TransformersAreRNNs:FastAutoregressiveTransformerswithLinearAttentionAngelosKatharopoulos12ApoorvVyas12NikolaosPappas3Franc¸oisFleuret12Abstractbytheglobalreceptiveﬁeldofself-attention,whichpro-c...
with Fast Are Linear Autoregressive
2023-11-14 21:46:52569429.81 KB26
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Neural Networks Are Convex Regularizers Exact Polynomial-time Convex Optimization Formulations for Two-Layer Networks
NeuralNetworksAreConvexRegularizers:ExactPolynomial-timeConvexOptimizationFormulationsforTwo-layerNetworksMertPilanci1TolgaErgen1Abstractconnectionstokernelmethods,andshowedthatrandomlyinitializedg...
Neural Networks Convex Exact Are
2023-11-14 21:45:2210341.87 MB22
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Interpretations Are Useful Penalizing Explanations to Align Neural Networks with Prior Knowledge
InterpretationsAreUseful:PenalizingExplanationstoAlignNeuralNetworkswithPriorKnowledgeLauraRieger1ChandanSingh2W.JamesMurdoch3BinYu23Abstractimproveamodel.ForanexplanationofadeeplearningmodeltoPred...
to Are Align Explanations Interpretations
2023-11-14 21:44:4219661.02 MB13
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The Odds Are Odd A Statistical Test for Detecting Adversarial Examples
TheOddsAreOdd:AStatisticalTestforDetectingAdversarialExamplesKevinRoth1YannicKilcher1ThomasHofmann1Abstract2017;Trame`retal.,2017).ThisappArentvulnerabilityisworrisomeasdeepnetsstarttoproliferatein...
for Test the Statistical Are
2023-11-13 14:48:468166.23 MB8
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Optimality Implies Kernel Sum Classifiers Are Statistically Efficient
OptimalityImpliesKernelSumClassiﬁersAreStatisticallyEfﬁcientRaphaelA.Meyer1JeanHonorio1AbstractweconsiderbinaryclassiﬁcationwithKernelSupportVec-torMachines(SVM),whichArecomputedbysolvingaWeprop...
Kernel Sum Are Classifiers Implies
2023-11-13 14:48:10905385.48 KB27
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Are Generative Classifiers More Robust to Adversarial Attacks
AreGenerativeClassiﬁersMoreRobusttoAdversarialAttacks?YingzhenLi1JohnBradshaw23YashSharma4AbstractAdversarialtraining,whichaugmentsthetrainingdatawithadversariallyperturbedinputs,hasshownmoderates...
Adversarial Generative Robust to Are
2023-11-13 14:46:2513921.46 MB20
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Adversarial Examples Are a Natural Consequence of Test Error in Noise
AdversarialExamplesAreaNaturalConsequenceofTestErrorinNoiseNicolasFord12JustinGilmer1NicholasCarlini1EkinD.Cubuk1Abstract(Rosenfeldetal.,2018).Atthesametime,theyArealsosensitivetosmall,worst-casepe...
of Adversarial Test Natural Are
2023-11-13 14:46:2112021.03 MB7
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When Samples Are Strategically Selected
WhenSamplesAreStrategicallySelectedHanruiZhang1YuCheng1VincentConitzer1Abstract1988;Friedman,1993)),thereisstilltheconcernthattheagentsendsonlyabiasedcollectionofsamples.IfwedoInstandardclassiﬁcat...
Are Samples When Strategically Selected
2023-11-13 14:46:081933258.49 KB17
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Tighter Variational Bounds Are Not Necessarily Better
TighterVariationalBoundsAreNotNecessarilyBetterTomRainforth1AdamR.Kosiorek12TuanAnhLe2ChrisJ.Maddison1MaximilianIgl2FrankWood3YeeWhyeTeh1Abstract&Kamp,1988;Hinton&Zemel,1994;Gregoretal.,2016;Chenet...
Variational bounds Are Better Not
2023-11-13 12:00:537591.03 MB22
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Spurious Local Minima Are Common in Two-Layer ReLU Neural Networks
SpuriousLocalMinimaAreCommoninTwo-LayerReLUNeuralNetworksItaySafran1OhadShamir1Abstractlearning,andtensordecomposition,donothavespuriouslocalminimaundersuitableassumptions,inwhichcaselo-Weconsidert...
Local in Are Spurious Minima
2023-11-13 12:00:42585385.45 KB20
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Not All Samples Are Created Equal Deep Learning with Importance Sampling
NotAllSamplesAreCreatedEqual:DeepLearningwithImportanceSamplingAngelosKatharopoulos12Franc¸oisFleuret12Abstractmodel.Tothisend,weproposeanovelimportancesamplingschemethatacceleratesthetrainingofan...
Deep Are Samples All Not
2023-11-13 12:00:17548534.6 KB20
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Strongly-Typed Agents Are Guaranteed to Interact Safely
Strongly-TypedAgentsAreGuaranteedtoInteractSafelyDavidBalduzzi1Abstractbeadaptedtoanotherwithoutadverseeffects?Theprob-lemsfallundermechanismdesign,abranchofgametheoryAsartiﬁcialagentsproliferate,...
to Are Safely Strongly-Typed Agents
2023-11-12 20:45:211238492.84 KB24
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How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices
HowCloseAretheEigenvectorsoftheSampleandActualCovarianceMatrices?AndreasLoukas1Abstractbetterwhenonlyasubsetofthespectrumisofinterest.Concretely,ourobjectiveistocharacterizehowmanysam-Howmanysample...
of the How Close Are
2023-11-12 20:44:311749441.96 KB19
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