Transcription of Model Compression - Cornell University
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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].}
a nonparametric bootstrap approach. For each attribute, a value is selected uniformly at random from the multiset (bag) of all valuesfor thatattributepresentinthetrain set.1 When the attribute values are generated independently, all conditional structure is lost and the pseudo examples are generated from a distribution that is usually much broader
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