In this lecture we will discuss **Deep Boltzmann Machines**.

**Please study the following material in preparation for the class:**

- Deep Boltzmann Machines. by Ruslan Salakhutdinov and Geoffrey Hinton
- Lecture slides (now linked)

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

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6 Replies to “Lecture 19, March 23rd, 2015: Deep Boltzmann Machines”

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IFT6266 – H2015 Representation Learning

A mostly deep learning course

In this lecture we will discuss **Deep Boltzmann Machines**.

**Please study the following material in preparation for the class:**

- Deep Boltzmann Machines. by Ruslan Salakhutdinov and Geoffrey Hinton
- Lecture slides (now linked)

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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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I am also confused about this point. Maybe you can go over it in detail Aaron?

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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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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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The energy function of the Deep Boltzmann Machine is a kind of challenging part for me. How did people come up with that energy functions?

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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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