On Fri Dec 19 23:17:23 UTC 2014, Aja Huang wrote:
We've just submitted our paper to ICLR. We made the draft available at
http://www.cs.toronto.edu/~cmaddis/pubs/deepgo.pdf
Cool... just out of curiosity, did a back-of-an-envelope estimation of the
cost of training your and Clark and Storkey's
Great work. Looks like the age of nn is here.
How does this compare in computation time to a heavy MC move generator?
One very minor quibble, I feel like a nag for even mentioning it: You write
The most frequently cited reason for the difficulty of Go, compared to
games such as Chess, Scrabble
On 20.12.2014 09:43, Stefan Kaitschick wrote:
If MC has shown anything, it's that computationally, it's much easier to
suggest a good move, than to evaluate the position.
Such can only mean an improper understanding of positional judgement.
Positional judgement depends on reading (or MC
Am Samstag, den 20.12.2014, 09:43 +0100 schrieb Stefan Kaitschick:
Great work. Looks like the age of nn is here.
How does this compare in computation time to a heavy MC move
generator?
One very minor quibble, I feel like a nag for even mentioning it: You
write
The most frequently
Hi Aja,
I hope you enjoy our work. Comments and questions are welcome.
I have three questions.
I don't understand minibatch.
Does CNN need 0.15sec for a positon, or 0.15sec for 128 positions?
ABCDEFGHJ
9. White(O) to move.
8...OO Previous Black move is H5(X)
7..XXXOO..
On 20.12.2014 11:21, Detlef Schmicker wrote:
it is not easy to get training data sets for an evaluation function?!
You seem be asking for abundant data sets, e.g., with triples Position,
Territory, Influence. Indeed, only dozens are available in the
literature and need a bit of extra work.
a good move, than to evaluate the position.
This is still true with your paper, it's just that the move suggestion has
become even better.
Stefan
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Hi,
There are things a CNN could probably do well, if only we had the right
database to train it. I have in mind these two possibilities:
* using a CNN as an evaluation function,
* using a CNN to estimate ownership for each point (i.e., a number
between -1 and 1 that is an estimate of who is
2014-12-20 11:33 GMT+00:00 Hiroshi Yamashita y...@bd.mbn.or.jp:
I don't understand minibatch.
Does CNN need 0.15sec for a positon, or 0.15sec for 128 positions?
0.15 sec for 128 positions.
ABCDEFGHJ
9. White(O) to move.
8...OO Previous Black move is H5(X)
7..XXXOO..
Hi Hiroshi,
2014-12-20 3:31 GMT+00:00 Hiroshi Yamashita y...@bd.mbn.or.jp:
But it looks playing strength is similar to Clark's CNN.
Against GnuGo our 12-layer CNN is about 300 Elo stronger (97% winning rate
against 86% based one the same KGS games). Against Fuego using their time
setting (10
Hi Álvaro,
I've done things like that, except I didn't use games by strong
computer opponents (none existed at the time), so just human amateur
games. In my experience the critical part is in learning about life
death. Once you have that, estimating ownership is fairly easy,
asymptotically
Hi,
I am still fighting with the NN slang, but why do you zero-padd the
output (page 3: 4 Architecture Training)?
From all I read up to now, most are zero-padding the input to make the
output fit 19x19?!
Thanks for the great work
Detlef
Am Freitag, den 19.12.2014, 23:17 + schrieb Aja
If you start with a 19x19 grid and you take convolutional filters of size
5x5 (as an example), you'll end up with a board of size 15x15, because a
5x5 box can be placed inside a 19x19 board in 15x15 different locations. We
can get 19x19 outputs if we allow the 5x5 box to be centered on any point,
Thanks for sharing. I'm intrigued by your strategy for integrating
with MCTS. It's clear that latency is a challenge for integration. Do
you have any statistics on how many searches new nodes had been
through by the time the predictor comes back with an estimation? Did
you try any prefetching
This would be very similar to the integration I do in Many Faces of Go. The
old engine provides a bias to move selection in the tree, but the old engine is
single threaded and only does a few hundred evaluations per second. I
typically get between 40 and 200 playouts through a node before Old
On Sat, Dec 20, 2014 at 9:35 PM, Robert Jasiek jas...@snafu.de wrote:
On 20.12.2014 17:04, Erik van der Werf wrote:
the critical part is in learning about life
death. Once you have that, estimating ownership is fairly easy
[...] See the following papers for more details: [...]
I think many of the programs have a mechanism for dealing with “slow”
knowledge. For example in Fuego, you can call a knowledge function for each
node that reaches some threshold T of playouts. The new technical challenge is
dealing with the GPU. I know nothing about it myself, but from what I
On Sat, Dec 20, 2014 at 6:16 AM, Hiroshi Yamashita y...@bd.mbn.or.jp wrote:
I put two commented games on
http://webdocs.cs.ualberta.ca/~mmueller/fuego/Convolutional-Neural-Network.html
Thank you for the report. It was fun.
I'm also surprised CNN can play move 185 in Game 1.
CNN uses 1, 2,
Aja wrote:
We haven't measured that but I think move history is an important feature
since Go is very much about answering the opponent's last move locally
(that's also why in Go we have the term tenuki for not answering the last
move).
I guess you could get some measure of the importance
I ran Fuego (latest svn version) on our machine (Intel(R) Xeon(R) CPU
Aja,
the 1.1 release of Fuego is from 2011 and much different from current svn
Fuego. Current version has many different kinds of knowledge added. It is also
much slower, maybe 5x. If you read my blog it explains this a
Hi Martin,
Thanks for the explanation. I'll write Fuego runs
2 (considering early stop) * 2.2 / 3.1
roughly 14200 playouts per move on the empty board using their time limits.
Thanks,
Aja
On Sat, Dec 20, 2014 at 11:27 PM, Martin Mueller mmuel...@ualberta.ca
wrote:
I ran Fuego (latest
Interesting looking paper: On correlation and budget constraints in
model-based bandit optimization with application to automatic machine
learning, Hoffman, Shahriari, de Freitas, AISTATS 2014
I can't say I've entirely understood yet, but I *think* that:
- targets scenario where there are many
Hi Mark,
2014-12-20 19:17 GMT+00:00 Mark Wagner wagner.mar...@gmail.com:
Thanks for sharing. I'm intrigued by your strategy for integrating
with MCTS. It's clear that latency is a challenge for integration. Do
you have any statistics on how many searches new nodes had been
through by the
Hi Aja,
It's more than 5000 playouts but less than 20k. Which version
I tried Fuego 1.1(2011, Windows version) on Intel Core i3 540,
2 cores 4 thread. 3.07GHz.
I played first 4 moves randomly, and next 4 moves are
GamesPlayed28952, Time 6.8, Games/s4249.8
GamesPlayed
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