unsupervised learning - Restricted Boltzmann Machine for real-valued data - gaussian linear units (glu) - -

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i want restricted boltzmann machine learn new representation of real-valued data (see: hinton - 2010 - practical guide training rbms). i'm struggling implementation of gaussian linear units.

with gaussian linear units in visible layer energy changes e(v,h)= ∑ (v-a)²/2σ - ∑ bh - ∑v/σ h w. don't know how change contrastive divergence learning algorithm. visible units won't sampled more linear. use expectation (mean-fied activation) p(v_i=1|h)= +∑hw + n(0,1) state. associations left unchangend ( pos: data*p(h=1|v)' neg: p(v=1|h)*p(h=1|v)' ). leads random noise when want reconstruct data. error rate stop improving around 50%.

finally want use gaussian linear units in both layers. how states of hidden units then? suggest using mean-field activation p(h_i=1|v)= b +∑vw + n(0,1) i'm not sure.

you take @ gaussian rbm hinton himself has provided please find here. http://www.cs.toronto.edu/~hinton/code/rbmhidlinear.m


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