IEEE Trans Pattern Anal Mach Intell - The Spike-and-Slab RBM and Extensions to Discrete and Sparse Data Distributions.

Tópicos

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Resumo

The spike-and-slab restricted Boltzmann machine (ssRBM) is defined to have both a real-valued "slab" variable and a binary "spike" variable associated with each unit in the hidden layer. The model uses its slab variables to model the conditional covariance of the observation -- thought to be important in capturing the statistical properties of natural images. In this paper, we present the canonical ssRBM framework together with some extensions. These extensions highlight the flexibility of the spike-and-slab RBM as a platform for exploring more sophisticated probabilistic models of high dimensional data in general and natural image data in particular. Here, we introduce the subspace-ssRBM focused on the task of learning invariant features. We highlight the behaviour of the ssRBM and its extensions through experiments with the MNIST digit recognition task and the CIFAR-10 object classification task.

Resumo Limpo

spikeandslab restrict boltzmann machin ssrbm defin realvalu slab variabl binari spike variabl associ unit hidden layer model use slab variabl model condit covari observ thought import captur statist properti natur imag paper present canon ssrbm framework togeth extens extens highlight flexibl spikeandslab rbm platform explor sophist probabilist model high dimension data general natur imag data particular introduc subspacessrbm focus task learn invari featur highlight behaviour ssrbm extens experi mnist digit recognit task cifar object classif task

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