Would the expected improvement be reduced training time or improved
accuracy?
2016-06-11 23:06 GMT+03:00 Stefan Kaitschick :
> If I understood it right, the playout NN in AlphaGo was created by using
> the same training set as the one used for the large NN that is used in the
> tree. There would
On Sun, Jun 12, 2016 at 10:51:37AM +0300, Petri Pitkanen wrote:
> 2016-06-11 23:06 GMT+03:00 Stefan Kaitschick :
>
> > If I understood it right, the playout NN in AlphaGo was created by using
> > the same training set as the one used for the large NN that is used in the
> > tree. There would be an
I don't know how the added training compares to direct training of the
shallow network.
It's prob. not so important, because both should be much faster than the
training of the deep NN.
Accuracy should be slightly improved.
Together, that might not justify the effort. But I think the fact that you
Might be worthwhile to try the faster, shallower policy network as a
MCTS replacement if it were fast enough to support enough breadth.
Could cut down on some of the scoring variations that confuse rather
than inform the score expectation.
On Sun, Jun 12, 2016 at 10:56 AM, Stefan Kaitschick
wrote
I don't understand the point of using the deeper network to train the
shallower one. If you had enough data to be able to train a model with many
parameters, you have enough to train a model with fewer parameters.
Álvaro.
On Sun, Jun 12, 2016 at 5:52 AM, Michael Markefka <
michael.marke...@gmail
I don't remember the content of the paper and currently can't look at the
PDF, but one possible explanation could be that a simple model trained
directly maybe regularizes differently from one trained on the best-fit
pre-smoothed output of a deeper net. The second could perhaps offer better
local o
Unfortunately I had to make some changes to
http://goratingserver.appspot.com , which broke the bot interface (updates
now come in via web sockets). However it should now be a lot easier to
connect a bot to the server as I created a jar file and configuration ini
file that connects a command line g
Have you considered using either of the two high level Go AIs (mentioned on
this email group this last week) as your end-of-game live-group estimator
(and could even use their scoring mechanism, too)?
On Sun, Jun 12, 2016 at 8:02 AM, Henry Hemming wrote:
> Unfortunately I had to make some chang
The purpose is to see if there is some sort of "simplification" available
to the emerged complex functions encoded in the weights. It is a typical
reductionist strategy, especially where there is an attempt to converge on
human conceptualization. Given the complexity of the nuances in Go, my
intuit
BTW, by improvement, I don't mean higher Go playing skill...I mean
appearing close to the same level of Go playing skill _per_ _move_ with far
less computational cost. It's the total game outcomes that will fall.
On Sun, Jun 12, 2016 at 3:55 PM, Jim O'Flaherty
wrote:
> The purpose is to see if t
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