Answers.Body: <p>For what it's worth, <a href="https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine" rel="nofollow noreferrer">Wikipedia</a> says that </p>
  
  <blockquote>
    <p>deep belief networks can be formed by "stacking" RBMs. </p>
  </blockquote>
  
  <p>Hinton <a href="http://www.scholarpedia.org/article/Deep_belief_networks#Deep_Belief_Nets_as_Compositions_of_Simple_Learning_Modules" rel="nofollow noreferrer">writes in Scholarpedia</a>: </p>
  
  <blockquote>
    <p>A deep belief net can be viewed as a composition of simple learning modules each of which is a restricted type of Boltzmann machine.</p>
  </blockquote>
  
  <p>So, a deep belief network is definitely a stacked RBM. </p>
  
  <p>I have never heard of different stacked RBMs, but it is easy to imagine something like convolutional stacked RBMs, where some RBMs are used as filters that slide over the input data or something. Whether that would still be called a deep belief network, is probably up to the guy who publishes it first. </p>
  
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Answers.Text: RBM is undirected. Shoud not DBN have directed units (umhh.. classifiers?) as top layers? Can be simple stacked RBM which is basically a feature detector, can be studied as a DBN?
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Answers.Text: What do you mean RBMs are undirected? They have an input layer and the input gets propagated to a hidden layer (and back during training). That looks pretty directed to me. I think BMs are undirected. If you want to do classification you'll put a classifier on top, that's correct.
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Answers.Text: By "undirected" i mean a possibility of input reconstruction from known feature vector (like with an autoencoder), which is not so straightforward for classifiers. Anyway, things became a bit clearer. Thank you:)
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Answers.Text: The possibility of input reconstruction from the hidden layer activation is how you train, but once you've learned your features the backward pass isn't used anymore. So then you just have an ordinary feedforward network.
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Body: <p>What is the difference between stacked restricted Boltzmann machine (RBM) and deep belief network (DBN)? </p>
  
  <p>Are they the same entity? If so, why? Is the latter some specific type of the former? If so, how to tell if stacked RBM is a DBN?</p>
  
Tags: <neural-networks><comparison><boltzmann-machine><deep-belief-network><restricted-boltzmann-machine>
Title: What is the difference between a stacked restricted Boltzmann machine and deep belief network?
UserDisplayName: Diligent Key Presser
ThreadText: # What is the difference between a stacked restricted Boltzmann machine and deep belief network?
  
  (Asked by: username_0 on 2016-12-31)
  
  What is the difference between stacked restricted Boltzmann machine (RBM) and deep belief network (DBN)?
  
  Are they the same entity? If so, why? Is the latter some specific type of the former? If so, how to tell if stacked RBM is a DBN?
  
  **Tags:** neural-networks, comparison, boltzmann-machine, deep-belief-network, restricted-boltzmann-machine
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2016-12-31. Score: 1 | Accepted answer
  
  For what it's worth, [Wikipedia](https://en.wikipedia.org/wiki/Restricted_Boltzmann_machine) says that
  
  > deep belief networks can be formed by "stacking" RBMs.
  
  Hinton [writes in Scholarpedia](http://www.scholarpedia.org/article/Deep_belief_networks#Deep_Belief_Nets_as_Compositions_of_Simple_Learning_Modules):
  
  > A deep belief net can be viewed as a composition of simple learning modules each of which is a restricted type of Boltzmann machine.
  
  So, a deep belief network is definitely a stacked RBM.
  
  I have never heard of different stacked RBMs, but it is easy to imagine something like convolutional stacked RBMs, where some RBMs are used as filters that slide over the input data or something. Whether that would still be called a deep belief network, is probably up to the guy who publishes it first.
  
  > **Comments:**
  > **username_0**: RBM is undirected. Shoud not DBN have directed units (umhh.. classifiers?) as top layers? Can be simple stacked RBM which is basically a feature detector, can be studied as a DBN?  
  > **username_1**: What do you mean RBMs are undirected? They have an input layer and the input gets propagated to a hidden layer (and back during training). That looks pretty directed to me. I think BMs are undirected. If you want to do classification you'll put a classifier on top, that's correct.  
  > **username_0**: By "undirected" i mean a possibility of input reconstruction from known feature vector (like with an autoencoder), which is not so straightforward for classifiers. Anyway, things became a bit clearer. Thank you:)  
  > **username_1**: The possibility of input reconstruction from the hidden layer activation is how you train, but once you've learned your features the backward pass isn't used anymore. So then you just have an ordinary feedforward network.  
  
  ---
  
  