Early Inference in Energy-Based Models Approximates Back-Propagation

9 Oct 2015Yoshua BengioAsja Fischer

We show that Langevin MCMC inference in an energy-based model with latent variables has the property that the early steps of inference, starting from a stationary point, correspond to propagating error gradients into internal layers, similarly to back-propagation. The error that is back-propagated is with respect to visible units that have received an outside driving force pushing them away from the stationary point... (read more)

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