Feedforward Initialization for Fast Inference of Deep Generative Networks is biologically plausible

6 Jun 2016Yoshua BengioBenjamin ScellierOlexa BilaniukJoao SacramentoWalter Senn

We consider deep multi-layered generative models such as Boltzmann machines or Hopfield nets in which computation (which implements inference) is both recurrent and stochastic, but where the recurrence is not to model sequential structure, only to perform computation. We find conditions under which a simple feedforward computation is a very good initialization for inference, after the input units are clamped to observed values... (read more)

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