Model Compression - Cornell University

ModelCompressionCristianBucil hundredsor thousandsof base-level classi , thespacerequiredto storethismany clas-si ers,andthetimerequiredto executethemat run-time,prohibitstheirusein applicationswheretestsetsarelarge( ),wherestoragespaceis ata premium( ),andwherecomputationalpower is limited( ).We present a method for\compressing"large,complexensemblesin to smaller,fastermodels,usuallywith-outsign i cant lossin Subject [PatternRe-cognition]:Models{ :Algorithms,Experimentation,Measure-ment , Performance, :SupervisedLearning, a collectionof modelswhosepredictionsarecombinedby weightedaveragingor beenthefocusof signi cant research in thepastdecade,anda variety of ensemblemethods have knownensemblemethods includebagging[2],boosting[14],randomfor ests[3],Bayesianaveraging[9]andstacking[ 17].}

General Terms: Algorithms, Experimentation, Measure-ment, Performance, Reliability. Keywords: Supervised Learning, Model Compression 1. INTRODUCTION An ensemble is a collection of models whose predictions are combined by weighted averaging or voting. Ensemble methods have been the focus of signi cant research in the

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  General, Model, Compression, Model compression

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