Good idea.
Section 13.2 in this tutorial
http://www.cs.toronto.edu/~hinton/absps/guideTR.pdf
<http://www.cs.toronto.edu/%7Ehinton/absps/guideTR.pdf> , explains some
standard techniques on dealing with real-valued visible nodes.
I also found a great answer here:
http://metaoptimize.com/qa/questions/12157/problems-with-dbn-for-audio-data
.
Maybe I could start working on the simple approach - 'Gaussian visible
nodes'.
On 9/16/2013 10:59 PM, Andreas Mueller wrote:
On 09/16/2013 09:54 PM, Issam wrote:
Reading the documentation, it seems that scikit's RBM does not
support continuous values or, more precisely, values that are larger
than one or less than zero; since logistic is the output function.
This paper, "http://www.ee.nthu.edu.tw/~hchen/pubs/iee2003.pdf
<http://www.ee.nthu.edu.tw/%7Ehchen/pubs/iee2003.pdf>", however,
explains how you can code RBM that supports continuous values -
adding zero-mean gaussian noise to the sampled value. It doesn't
seem difficult.
Did someone open a pull-request that adds this functionality?
If no, then I can to work on it ;)
I think no-one is working on it at the moment.
I guess it would be a nice feature, though. I was not aware of the
reference. Is it the standard reference?
I'd go by what the deep learning tutorials are doing.
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