 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| • |
The network
learns the constraints even if 10% of the
|
|
|
inputs are
missing.
|
|
|
|
– |
First
fill in the missing inputs randomly
|
|
|
|
– |
Then
use the back-propagated energy derivatives to
|
|
|
slowly
change the filled-in values until they fit in with
|
|
|
the
learned constraints.
|
|
|
| • |
Why don’t the
corrupted inputs interfere with the
learning
|
|
of the
constraints?
|
|
|
|
– |
The
energy function has a small slope when the
|
|
|
constraint
is violated by a lot.
|
|
|
|
– |
So
when a constraint is violated by a lot it does not
|
|
|
adapt.
|
|
|
|
• |
Don’t
learn when things don’t make sense.
|
|