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Quantifying and reducing Bias in Maximum Likelihood Estimation of Structured Anomalies
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ActNN reducing Training Memory Footprint via 2-Bit Activation Compressed Training
ActNN:reducingTrainingMemoryFootprintvia2-BitActivationCompressedTrainingJianfeiChen1LianminZheng1ZheweiYao1DequanWang1IonStoica1MichaelW.Mahoney1JosephE.Gonzalez1AbstractTrainingThroughput60L0L1L3...
via Memory Training reducing ActNN
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reducing Sampling Error in Batch Temporal Difference Learning
reducingSamplingErrorinBatchTemporalDifferenceLearningBrahmaS.Pavse1IshanDurugkar1JosiahP.Hanna23PeterStone14Abstractpolicy(Puterman&Shin,1978;Bertsekas,1987;Konda&Tsitsiklis,2000).Thesealgorithmsr...
Sampling in Batch Temporal Error
2023-11-14 21:46:061565752.15 KB19
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DropNet reducing Neural Network Complexity via Iterative Pruning
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Neural Complexity via Network Iterative
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The advantages of multiple classes for reducing overfitting from test set reuse
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of for the Multiple Classes
2023-11-13 14:48:441193381.2 KB26
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Quantifying and reducing Bias in Maximum Likelihood Estimation of Structured Anomalies

ActNN reducing Training Memory Footprint via 2-Bit Activation Compressed Training

reducing Sampling Error in Batch Temporal Difference Learning

DropNet reducing Neural Network Complexity via Iterative Pruning

The advantages of multiple classes for reducing overfitting from test set reuse