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Questions tagged [restricted-boltzmann-machine]

For questions related to restricted Boltzmann machines, which is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.

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How do we calculate the hidden units values in a (restricted) Boltzmann machine?

Now, Boltzmann machines are energy-based undirected networks, meaning there are no forward computations. Instead, for each input configuration $x$, a scalar energy is calculated to asses this ...
Achraf Oussidi's user avatar
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Purpose of the hidden variables in a Restricted Boltzmann Machine

From the part titled Introducing Latent Variables under subsection 2.2 in this tutorial: Introducing Latent Variables. Suppose we want to model an $m$-dimensional unknown probability distribution $q$ ...
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How can I reconstruct sparse one-hot encodings using an RBM?

I am currently working with a categorical-binary RBM, where there are 50 categorical visible units and 25 binary hidden units. The categorical visible units are expressed in one-hot encoding format, ...
mhdadk's user avatar
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How do I derive the gradient of the log-likelihood of an RBM?

In a Restricted Boltzmann Machine (RBM), the likelihood function is: $$p(\mathbf{v};\mathbf{\theta}) = \frac{1}{Z} \sum_{\mathbf{h}} e^{-E(\mathbf{v},\mathbf{h};\mathbf{\theta})}$$ Where $E$ is the ...
mhdadk's user avatar
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How Restricted Boltzman Machine (RBM) generates hand-written digit?

I am reading RBMs from this paper. In Fig1 they show an example of generating hand-written digit using RBMs. This is the figure they are showing: In the learning step first we sample $h$ from $h \sim ...
Swakshar Deb's user avatar
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Best/quickest approach for tuning the hyperparameters of a restricted boltzmann machine

I have an RBM model which takes extremely long to train and evaluate because of the large number of free parameters and the large amount of input data. What would be the most efficient way of tuning ...
Žiga Stupan's user avatar
1 vote
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How do I sample conditionally from deep belief networks?

Deep belief networks (DBNs) are generative models, where, usually, you sample by thermalising the deepest layer (as it's a restricted Boltzmann machine), and then forward propagating a sample towards ...
Abelaer's user avatar
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What are the differences between a deep belief network, a restricted Boltzmann machine and a deep Boltzmann machine?

Can anyone list the differences between deep Belief network (DBN), restricted Boltzmann machine (RBM), deep Boltzmann machine (DBM) using simple examples? Links to other resources are also ...
Pluviophile's user avatar
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