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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. Variationalmethodsusesimpleapproximation stothetrueconditionaldistribution,butthe approximationsmaybepoor, , describea modelinwhichthetoptwo hiddenlayersformanundirectedassociative memory(seefigure1)andthe To appearinNeuralComputation2006remaininghi ddenlayersforma directedacyclicgraphthatconvertstherepre sentationsintheassociative a fast,greedylearningalgorithmthatcanfinda fairlygoodsetofparametersquickly, evenindeepnetworkswithmillionsofparamete rsandmany unsupervisedbutcanbeap-pl

1. There is a fast, greedy learning algorithm that can find a fairly good set of parameters quickly, even in deep networks with millions of parameters and many hidden layers. 2. The learning algorithm is unsupervised but can be ap-plied to labeled data by learning a model that generates both the label and the data. 3.

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