"All"的相关文档

标签“All”的相关文档，共12条

When All We Need is a Piece of the Pie A Generic Framework for Optimizing Two-way Partial AUC
WhenAllWeNeedisaPieceofthePie:AGenericFrameworkforOptimizingTwo-wayPartialAUCZhiyongYang12QianqianXu3ShilongBao12YuanHe4XiaochunCao12QingmingHuang3567Abstractframework.TheAreaUndertheROCCurve(AUC)i...
of is When All We
2023-11-16 19:41:277931.76 MB27
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One for One, or All for All Equilibria and Optimality of Collaboration in Federated Learning
OneforOne,orAllforAll:EquilibriaandOptimalityofCollaborationinFederatedLearningAvrimBlum1NikaHaghtalab2RichardLanasPhillips3HanShao1Abstractpitals(Wenetal.,2019;Powell,2019)anddevices(McMa-han&Rama...
for and All One Optimality
2023-11-16 19:28:231616380.75 KB21
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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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Is Space-Time Attention All You Need for Video Understanding
IsSpace-TimeAttentionAllYouNeedforVideoUnderstanding?GedasBertasius1HengWang1LorenzoTorresani12AbstractVideounderstandingsharesseveralhigh-levelsimilaritieswithNLP.FirstofAll,videosandsentencesareb...
for Attention is All Need
2023-11-16 18:47:0515338.17 MB11
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Attention is not All you need pure attention loses rank doubly exponentiAlly with depth
AttentionisnotAllyouneed:pureattentionlosesrankdoublyexponentiAllywithdepthYiheDong1Jean-BaptisteCordonnier2AndreasLoukas3Abstractattentionlayers.Surprisingly,weﬁndthatpureself-attentionnetworks(S...
Attention is All Not Need
2023-11-16 18:00:189421.2 MB21
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Rigging the Lottery Making All Tickets Winners
RiggingtheLottery:MakingAllTicketsWinnersUtkuEvci1TrevorGale1JacobMenick2PabloSamelCastro1ErichElsen2AbstractFigure1.RigLimprovestheoptimizationofsparseneuralnet-worksbyleveragingweightmagnitudeand...
the Making All Lottery Rigging
2023-11-14 21:46:1219111.17 MB20
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Randomized Smoothing of All Shapes and Sizes
RandomizedSmoothingofAllShapesandSizesGregYang1TonyDuan12J.EdwardHu12HadiSalman1IlyaRazenshteyn1JerryLi1Abstractheuristicdefensesthatarerobusttospeciﬁcclassesofper-turbations,butmanywouldlaterbebr...
of and Randomized All Smoothing
2023-11-14 21:46:049741.52 MB4
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One Size Fits All Can We Train One Denoiser for All Noise Levels
OneSizeFitsAll:CanWeTrainOneDenoiserforAllNoiseLevels?AbhiramGnansambandam1StanleyH.Chan12AbstractarguablyuniversalforAlllearning-basedestimators.Whensuchaproblemarises,themoststraight-forwardsolut...
Can All One We Size
2023-11-14 21:45:377451 MB18
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All in the (Exponential) Family Information Geometry and Thermodynamic Variational Inference
AllintheExponentialFamily:BregmanDualityinThermodynamicVariationalInferenceRobBrekelmans1VadenMasrani2FrankWood2GregVerSteeg1AramGalstyan1AbstractFigure1.TheoriginalTVOpaperrecommendedusingtwoparti...
the in Geometry Information Exponential
2023-11-14 21:43:0419587.58 MB9
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Submodular Streaming in All Its Glory Tight Approximation, Minimum Memory and Low Adaptive Complexity
SubmodularStreaminginAllItsGlory:TightApproximation,MinimumMemoryandLowAdaptiveComplexityEhsanKazemi1MarkoMitrovic1MortezaZadimoghaddam2SilvioLattanzi2AminKarbasi1Abstractnon-negativesetfunctionf:2...
Submodular in Tight All its
2023-11-13 14:48:419321.99 MB17
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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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Deep Linear Networks with Arbitrary Loss All Local Minima Are Global
DeepLinearNetworkswithArbitraryLoss:AllLocalMinimaAreGlobalThomasLaurent1JamesH.vonBrecht2Abstractdeepnonlinearnetworksduringtraining(Saxeetal.,2014).ResultsofthissortprovideasmAllpieceofevidenceth...
Networks with Deep Loss Linear
2023-11-13 11:59:241994267.6 KB15
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