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Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information
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Learning Representations by Discrete Structured
2023-11-14 21:44:521602314.04 KB12
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Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications
Memory-OptimalDirectConvolutionsforMaximizingClassiﬁcationAccuracyinEmbeddedApplicationsAlbertGural1BorisMurmann1Abstract(IoT),themachinelearningmodelistrainedonaserverandsubsequentlydeployedacros...
for Classification Maximizing Convolutions Direct
2023-11-13 14:47:508347.43 MB10
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Greed is Still Good Maximizing Monotone Submodular+Supermodular (BP) Functions
GreedisStillGood:MaximizingMonotoneSubmodular+Supermodular(BP)FunctionsWenruoBai1JeffreyA.Bilmes12Abstractoptimalchoiceateachstageinthehopeofﬁndingagoodglobalsolution.Itisoneofthesimplest,mostwide...
Good is Maximizing Greed Still
2023-11-13 11:59:43886724.57 KB25
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Learning to Aggregate Ordinal Labels by Maximizing Separating Width
LearningtoAggregateOrdinalLabelsbyMaximizingSeparatingWidthGuangyongChen1ShengyuZhang1DiLin2HuiHuang2PhengAnnHeng13Abstractsourcingsystemsresorttodistributingeachitemtoanum-berofredundantworkers.Th...
Learning by to Maximizing Aggregate
2023-11-12 20:44:42843644.29 KB28
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Learning Discrete Representations via Information Maximizing Self-Augmented Training
LearningDiscreteRepresentationsviaInformationMaximizingSelf-AugmentedTrainingWeihuaHu12TakeruMiyato34SeiyaTokui31EiichiMatsumoto31MasashiSugiyama21Abstract000111111DiscreterepresentationsSimilariza...
Learning Representations via Discrete Information
2023-11-12 20:44:391348474.61 KB12
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Learning Discrete Structured Representations by Adversarially Maximizing Mutual Information

Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications

Greed is Still Good Maximizing Monotone Submodular+Supermodular (BP) Functions

Learning to Aggregate Ordinal Labels by Maximizing Separating Width

Learning Discrete Representations via Information Maximizing Self-Augmented Training