Visualizing a Trained Autoencoder

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We will visualize the function computed by hidden unit <math>\textstyle i</math>---which depends on the
We will visualize the function computed by hidden unit <math>\textstyle i</math>---which depends on the
parameters <math>\textstyle W^{(1)}_{ij}</math> (ignoring
parameters <math>\textstyle W^{(1)}_{ij}</math> (ignoring
-
the bias term for now) using a 2D image.  In particular, we think of
+
the bias term for now)---using a 2D image.  In particular, we think of
<math>\textstyle a^{(2)}_i</math> as some non-linear feature of the input <math>\textstyle x</math>.
<math>\textstyle a^{(2)}_i</math> as some non-linear feature of the input <math>\textstyle x</math>.
We ask:
We ask:

Revision as of 00:34, 4 May 2011

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