Lecture 19, March 23rd, 2015: Deep Boltzmann Machines

In this lecture we will discuss Deep Boltzmann Machines.

Please study the following material in preparation for the class:

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6 thoughts on “Lecture 19, March 23rd, 2015: Deep Boltzmann Machines”

  1. I don’t quite understand the training procedure presented in the paper. When pre-training each layer of the DBM, twice as many parameters are computed than in the final model. How are those twice as many parameters re-combined to produce only one copy of the layer? Does the Gibbs sampler just discard the modified layer and preserve the other one as a sample?

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  2. People who talk about this paper often mention the fact that the actual implementation uses an array of hacks/tricks that cannot be inferred from the paper. (Ian Goodfellow mentioned this in his PhD defense).

    I’d be nice if someone could go over these tricks.

    Kelvin

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  3. In the Paper Figure.4 they show examples of samples obtained from the 2 hidden layers and the 3 hidden layers DBM, should we see a difference in quality of the numbers (they look pretty good in the 2 models…)?
    if this is right what’s the advantage/interpretation? do we only need more then 2 layer for more complex representation in MNIST the 2 layer would be a good estimator of the distribution.

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    1. My intuition is that it follows the Boltzmann Machine’s energy function (Eq 1. in the paper) but filling with zeros the weights corresponding to nodes that are not connected.

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