I think that's ok: the prediction systems are already used to deal with
a huge number of positions during training, it's just a matter of
changing the quality of these positions. Say instead of training on 100%
good answers to good moves from games, we could take half as many and
train on 50%
My comment was addressed to the original question, which mentioned more
traditional pattern based work, such as Remi’s.
Let’s think about how you might build a NN using a large pattern base as inputs.
A NN has K features per point on the board, and you don’t want K to be a large
number.
On 17-04-17 15:04, David Wu wrote:
> If you want an example of this actually mattering, here's example where
> Leela makes a big mistake in a game that I think is due to this kind of
> issue.
Ladders have specific treatment in the engine (which also has both known
limitations and actual bugs in
Now, I love this idea. A super fast cheap pattern matcher can act as input
into a neural network input layer in sort of a "pay additional attention
here and here and...".
On Apr 18, 2017 6:31 AM, "Brian Sheppard via Computer-go" <
computer-go@computer-go.org> wrote:
Adding patterns is very
Adding patterns is very cheap: encode the patterns as an if/else tree, and it
is O(log n) to match.
Pattern matching as such did not show up as a significant component of Pebbles.
But that is mostly because all of the machinery that makes pattern-matching
cheap (incremental updating of 3x3
