"Layers"的相关文档

Training Graph Neural Networks with 1000 Layers
TrainingGraphNeuralNetworkswith1000LayersGuohaoLi12MatthiasMu¨ller1BernardGhanem2VladlenKoltun1AbstractRevGNN-WideResGNN-224Deepgraphneuralnetworks(GNNs)have88achievedexcellentresultsonvarioustask...
Neural Networks with Graph Training
2023-11-16 19:42:1314264.07 MB11
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Lipschitz normalization for self-attention Layers with application to graph neural networks
LipschitzNormalizationforSelf-AttentionLayerswithApplicationtoGraphNeuralNetworksGeorgeDasoulas12KevinScaman1AladinVirmaux1Abstractclassification(Velickovicetal.,2018;Lietal.,2016)andcomputervision...
for with Lipschitz Normalization Application
2023-11-16 19:05:098452.28 MB19
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Leveraging Sparse Linear Layers for Debuggable Deep Networks
LeveragingSparseLinearLayersforDebuggableDeepNetworksEricWong1ShibaniSanturkar1AleksanderMa˛dry1Abstractmodel’sfailuremodesorevaluatecorrectiveinterventionswithoutin-depthproblem-speciﬁcstudies....
for Sparse Deep Linear Leveraging
2023-11-16 19:05:0810496.34 MB18
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Global Optimality Beyond Two Layers Training Deep ReLU Networks via Convex Programs
GlobalOptimalityBeyondTwoLayers:TrainingDeepReLUNetworksviaConvexProgramsTolgaErgen1MertPilanci1AbstractOutputUnderstandingthefundamentalmechanismbe-Inputhindthesuccessofdeepneuralnetworksisoneofth...
Deep Beyond Global Two Training
2023-11-16 18:46:4710981.3 MB8
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BASE Layers Simplifying Training of Large, Sparse Models
BASELayers:SimplifyingTrainingofLarge,SparseModelsMikeLewis1ShrutiBhosale1TimDettmers12NamanGoyal1LukeZettlemoyer12AbstractWorker1Worker2Weintroduceanewbalancedassignmentofex-Re-routetooriginalwork...
of Sparse Training Large Base
2023-11-16 18:07:41905199.92 KB19
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Deep Residual Output Layers for Neural Language Generation
DeepResidualOutputLayersforNeuralLanguageGenerationNikolaosPappas1JamesHenderson1Abstractbeddingstocapturethesimilaritystructureoftheoutputlabelspace,sothatdataforsimilarlabelscanhelpclassi-Manytas...
Neural for Deep Language Residual
2023-11-13 14:46:51632380.84 KB3
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BERT and PALs Projected Attention Layers for Efficient Adaptation in Multi-Task Learning
BERTandPALs:ProjectedAttentionLayersforEfﬁcientAdaptationinMulti-TaskLearningAsaCooperStickland1IainMurray1AbstractHowever,ﬁne-tuningseparatemodelsforeachtaskoftenworksbetterinpractice.Althoughwe...
for and Attention Projected BERT
2023-11-13 14:46:31717251.8 KB18
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