[FRIAM] All Your Them Are One Model To Us Learn

[Roger Critchlow]

Different kind of model, Merle, for a deep neural net the model is the
architecture of untrained layers and the connections between them.  Then
you train the model on a discrimination task, like distinguishing
labradoodles from fried chicken.  So this doesn't mean much to simulation
modelers for the moment.

-- rec --

[image: Inline image 1]

On Tue, Jun 20, 2017 at 11:56 AM, Merle Lefkoff <merlelefkoff at gmail.com>
wrote:

> Wow, Roger.  I don't get all this, but tell me---does this change
> everything for modelers?
>
> On Tue, Jun 20, 2017 at 9:39 AM, Roger Critchlow <rec at elf.org> wrote:
>
>> Apropos my babbling about all patterns being patterns and all the
>> mechanisms that recognize patterns being an incomprehensible jumble of
>> mechanisms, Google shares One Model to Learn Them All,
>> https://arxiv.org/abs/1706.05137, in which it turns out that throwing
>> all the architectural elements from all kinds of deep learning into a
>> single model ends up working pretty well.  Adding the recursive elements
>> used to parse the linear sequences of elements in languages never hurts and
>> mostly improves the performance of image classifiers and object
>> recognizers.  Go figure.
>>
>> Found from https://venturebeat.com/2017/06/19/google-advances-ai-w
>> ith-one-model-to-learn-them-all/amp/
>>
>> -- rec --
>>
>>
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>
>
>
> --
> Merle Lefkoff, Ph.D.
> President, Center for Emergent Diplomacy
> emergentdiplomacy.org
> Santa Fe, New Mexico, USA
>
> Visiting Professor in Integrative Peacebuilding
> Saint Paul University
> Ottawa, Ontario, Canada
>
> merlelefkoff at gmail.com <merlelefoff at gmail.com>
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>
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