Neural Comput - An efficient learning procedure for deep Boltzmann machines.

Tópicos

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{ state(1844) use(1261) util(961) }
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{ patient(2837) hospit(1953) medic(668) }
{ model(2656) set(1616) predict(1553) }
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{ age(1611) year(1155) adult(843) }
{ medic(1828) order(1363) alert(1069) }
{ signal(2180) analysi(812) frequenc(800) }
{ cost(1906) reduc(1198) effect(832) }
{ group(2977) signific(1463) compar(1072) }
{ sampl(1606) size(1419) use(1276) }
{ gene(2352) biolog(1181) express(1162) }
{ first(2504) two(1366) second(1323) }
{ intervent(3218) particip(2042) group(1664) }
{ activ(1138) subject(705) human(624) }
{ patient(1821) servic(1111) care(1106) }
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{ detect(2391) sensit(1101) algorithm(908) }

Resumo

We present a new learning algorithm for Boltzmann machines that contain many layers of hidden variables. Data-dependent statistics are estimated using a variational approximation that tends to focus on a single mode, and data-independent statistics are estimated using persistent Markov chains. The use of two quite different techniques for estimating the two types of statistic that enter into the gradient of the log likelihood makes it practical to learn Boltzmann machines with multiple hidden layers and millions of parameters. The learning can be made more efficient by using a layer-by-layer pretraining phase that initializes the weights sensibly. The pretraining also allows the variational inference to be initialized sensibly with a single bottom-up pass. We present results on the MNIST and NORB data sets showing that deep Boltzmann machines learn very good generative models of handwritten digits and 3D objects. We also show that the features discovered by deep Boltzmann machines are a very effective way to initialize the hidden layers of feedforward neural nets, which are then discriminatively fine-tuned.

Resumo Limpo

present new learn algorithm boltzmann machin contain mani layer hidden variabl datadepend statist estim use variat approxim tend focus singl mode dataindepend statist estim use persist markov chain use two quit differ techniqu estim two type statist enter gradient log likelihood make practic learn boltzmann machin multipl hidden layer million paramet learn can made effici use layerbylay pretrain phase initi weight sensibl pretrain also allow variat infer initi sensibl singl bottomup pass present result mnist norb data set show deep boltzmann machin learn good generat model handwritten digit d object also show featur discov deep boltzmann machin effect way initi hidden layer feedforward neural net discrimin finetun

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