One-shot learning

[Bo Xu]

Ross Gayler wrote:

>To summarise: IF you want to perform cognitive tasks THEN 'in principle' one
>shot learning is the only training regime that is acceptable (although slower
>learning may be required to get the net to the point where it can learn in
>one shot).  All you have to do is invent a good one-shot learning scheme :-).

Although one-shot (-trial) learning may not be the only mode of learning in
our cognitive processes, it's true that the learning in our cognitive
processes will not take as many times (epochs) as current BPNN takes.
One-shot learning can be served as a goal and a criterion for learning schemes
in both cognitive learning processes as well as learning systems for practical
applications.

Our work on PPNN (I posted the abstract several days ago) was originally driven
by the one-trial learning.  Although PPNN has not reached one-trial learning,
it has stepped closer to it.

In order to contrast the topological effect, we constrained PPNN to be the
same as BPNN in all aspects except the topology. It was shown that the
stereotopology alone can increase the training times (epoches) by several
orders (due to the characteristics of PPNN's stereotopology, we used the
average training time instead of epochs to measure the rate of convergence).
It was found that the more difficult the problem is, the higher the order is.
This topological speedup lies in the fact that there is a cause of slowness
in the original planar topology of BPNN that cannot be accounted for by the
learning algorithm or units characteristics (no matter what learning algorithm
is used or what units responsive characteristics are employed, this cause of
slow learning always exists.  It is inherent to the planar topology of BPNN).


Bo Xu
Indiana University
itgt500 at indycms.iupui.edu