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A fast learning algorithm for deep belief nets

A fastlearningalgorithmfordeepbeliefnets TehDepartmentofComputerScienceNationalUn iversityofSingapore3 ScienceDrive 3, showhowtouse complementarypriors toeliminatetheexplainingawayeffectsthatm akeinferencedifficultindensely-connected beliefnetsthathave many ,wederive a fast,greedyalgo-rithmthatcanlearndeep,di rectedbeliefnetworksonelayerata time,providedthetoptwo lay-ersformanundirectedassociative memory. Thefast,greedyalgorithmis usedto initializea slowerlearningprocedurethatfine-tunesthe weightsus-inga contrastive ,a networkwiththreehiddenlayersformsa verygoodgenerative modelgivesbetterdigitclassificationthant hebestdiscrimi-native associative memoryandit is easytoex-ploretheseravinesbyusingthedire ctedconnec-tionstodisplaywhattheassociat ive difficultin densely-connected,directedbeliefnetsthat have many hiddenlayersbecauseit is difficultto infertheconditionaldistributionofthehidd enactivitieswhengivenadatavector.

long ravines in the free-energy landscape of the top-level associative memory and it is easy to ex-plore these ravines by using the directed connec-tions to display what the associative memory has in mind. 1 Introduction Learning is difficult in densely-connected, directed belief nets that have many hidden layers because it is difficult to ...

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