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Cuchulainn
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Re: Machine Learning and the physical sciences

May 23rd, 2019, 3:52 pm

maestro,
I took your advice and found this

http://www.physics.ox.ac.uk/phystat05/p ... stat05.pdf

Initial results are promising .. 10 hours for 10,000 samples.

Do you have an opinion yourself? Enlighten us. What's new, apart from the cute name?

I bet you won't give a technical answer.
Step over the gap, not into it. Watch the space between platform and train.
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katastrofa
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Re: Machine Learning and the physical sciences

May 23rd, 2019, 4:43 pm

Small dogs bark the loudest. No wonder everyone ignores you. Why don't you say something righteous and hopeful for a change?
Old crazy dogs try to bite the air. With no teeth.
 
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katastrofa
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Re: Machine Learning and the physical sciences

May 23rd, 2019, 7:09 pm

maestro,
I took your advice and found this

http://www.physics.ox.ac.uk/phystat05/p ... stat05.pdf

Initial results are promising .. 10 hours for 10,000 samples.

Do you have an opinion yourself? Enlighten us. What's new, apart from the cute name?

I bet you won't give a technical answer.
Since you both seem to know virtually nothing about this stuff:
In Bayesian inference you can estimate not only the parameters P of your hypothesised model M, but also perform the model comparison (selection). The full Beyes' rule us p(P | data, M) = p(data | P, M) * p(P | M) / p(data | M), where p(data | M) is called "model evidence". Yo usolve the inverse problem p(M_i | data) = p(data | M_i) / \sum_j p(data | M_j) to find the best model, namely the one with the highest p(M_i | data) (which translates to the highest evidence given data).
Bayesian *neural* networks implement the above procedure in addition to the standard parameter fitting.

xx
 
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Cuchulainn
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Re: Machine Learning and the physical sciences

May 24th, 2019, 1:55 pm

You seem to be using the usual Bayes' theorem for learning. I recall

[$]p(P | data, M) = p(data | P, M) * p(P|M) / p(data | M)[$]

Bhat and Prosper have a slightly different rule (see their equation) wrt prior

[$]p(P | data, M) = p(data | P, M) * p(P) / p(data | M)[$]

Does this trick warrant coining yet another name? It feels like NN++ overloading?


Bayesian *neural* networks implement the above procedure in addition to the standard parameter fitting.
Who uses this and where? I would be interested in an explanation in addition to this general description. 

Very few articles describe the 'how to' step-by-step process/algorithm from input to output.
Step over the gap, not into it. Watch the space between platform and train.
http://www.datasimfinancial.com
http://www.datasim.nl
 
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katastrofa
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Re: Machine Learning and the physical sciences

May 25th, 2019, 4:03 pm

If the prior in the same in all models, then you can use the formula from those guys' work, obvsly.
I can't answer the question about the ML nomenclature. It amazes me too.

The AI craze seems to use BNNs to calculate the uncertainty of the estimated weights. I use it for model selection/testing strategies/etc., as stated above. I used the ML methods twice in the last 2 years. I'm not sure if what I'm doing isn't called ML now, though.

I'll name the longest maturing cheese in my fridge in your honour - Sniffy Irish...
 
 
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Cuchulainn
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Re: Machine Learning and the physical sciences

September 19th, 2019, 8:13 am

Are they looking for VC funding?
Agent technology was hype around 2001, especially mobile agents. Ir's too difficult.
Step over the gap, not into it. Watch the space between platform and train.
http://www.datasimfinancial.com
http://www.datasim.nl
 
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katastrofa
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Re: Machine Learning and the physical sciences

September 19th, 2019, 10:52 am

Are they looking for VC funding?
Agent technology was hype around 2001, especially mobile agents. Ir's too difficult.
It's OpenAI/Musk - always looking for VC :-)
It actually shows that reinforcement learning with deep networks is a way to solving simple tasks. Now it's the matter of increasing the complexity while controlling the error rate.
 
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Cuchulainn
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Re: Machine Learning and the physical sciences

September 19th, 2019, 12:12 pm

Are they looking for VC funding?
Agent technology was hype around 2001, especially mobile agents. Ir's too difficult.
It's OpenAI/Musk - always looking for VC :-)
It actually shows that reinforcement learning with deep networks is a way to solving simple tasks. Now it's the matter of increasing the complexity while controlling the error rate.
The devil is in the details.
Step over the gap, not into it. Watch the space between platform and train.
http://www.datasimfinancial.com
http://www.datasim.nl
 
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katastrofa
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Re: Machine Learning and the physical sciences

September 19th, 2019, 12:42 pm

IMHO, we should stop looking at the details, which become inexplicable, and prepare ourselves for the major event: the arrival of our future overlord, the AI!
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