Neural Comput - Enhanced gradient for training restricted Boltzmann machines.

Tópicos

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Resumo

Restricted Boltzmann machines (RBMs) are often used as building blocks in greedy learning of deep networks. However, training this simple model can be laborious. Traditional learning algorithms often converge only with the right choice of metaparameters that specify, for example, learning rate scheduling and the scale of the initial weights. They are also sensitive to specific data representation. An equivalent RBM can be obtained by flipping some bits and changing the weights and biases accordingly, but traditional learning rules are not invariant to such transformations. Without careful tuning of these training settings, traditional algorithms can easily get stuck or even diverge. In this letter, we present an enhanced gradient that is derived to be invariant to bit-flipping transformations. We experimentally show that the enhanced gradient yields more stable training of RBMs both when used with a fixed learning rate and an adaptive one.

Resumo Limpo

restrict boltzmann machin rbms often use build block greedi learn deep network howev train simpl model can labori tradit learn algorithm often converg right choic metaparamet specifi exampl learn rate schedul scale initi weight also sensit specif data represent equival rbm can obtain flip bit chang weight bias accord tradit learn rule invari transform without care tune train set tradit algorithm can easili get stuck even diverg letter present enhanc gradient deriv invari bitflip transform experiment show enhanc gradient yield stabl train rbms use fix learn rate adapt one

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