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Minimizing Description Length in an Unsupervised Neural Network

Geoffrey E. Hinton
Department of Computer Science
University of Toronto

Richard Zemel
Computational Neurobiology Laboratory
The Salk Institute
La Jolla


An autoencoder network uses a set of recognition weights to convert an input vector into a representation vector.  It then uses a set of generative weights to convert the representation vector into an approximate reconstruction of the input vector.  We derive an objective function for training autoencoders based on the Minimum Description Length (MDL) principle.  The aim is to minimize the information required to describe both the representation vector and the reconstruction error.  This information is minimized by choosing representation vectors stochastically according to a Boltzmann distribution.  Unfortunately, if the representation vectors use distributed representations, it is exponentially expensive to compute this Boltzmann distribution because it involves all possible representation vectors.  We show that the recognition weights of an autoencoder can be used to compute an approximation to the Boltzmann distribution.  This approximation corresponds to using a suboptimal encoding scheme and therefore gives an upper bound on the minimal description length.  Even when this bound is poor, it can be used as a Lyapunov function for learning both the generative and the recognition weights.  We   demonstrate that this approach can be used to learn distributed representations in which many different hidden causes combine to produce each observed data vector.   Such representations can be exponentially more efficient in their use of hardware than standard vector quantization or mixture models.

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