Abstract
I define a latent variable model in the form of a neural network for which only target outputs are specified; the inputs are unspecified. Although the inputs are missing, it is still possible to train this model by placing a simple probability distribution on the unknown inputs and maximizing the probability of the data given the parameters. The model can then discover for itself a description of the data in terms of an underlying latent variable space of lower dimensionality. I present preliminary results of the application of these models to protein data.
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© 1996 Kluwer Academic Publishers
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MacKay, D.J.C. (1996). Density Networks and their Application to Protein Modelling. In: Skilling, J., Sibisi, S. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 70. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-0107-0_28
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DOI: https://doi.org/10.1007/978-94-009-0107-0_28
Publisher Name: Springer, Dordrecht
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