
Expressive Temperature - rhema
https://www.robinsloan.com/expressive-temperature/
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DoctorOetker
one thing is phrased a bit confusing: the statistical weights are supposedly
"divided by temperature", but if all probabilities are scaled with some factor
and renormalized you end up with the original weight distribution.

the author obviously intends to denote that the weights are similar to the
weights in statistical mechanics and proportional to exp(-E/(kT))

In example given any 2 outcomes A and B from the distribution at temperature
"1.0" with probability ratio P_A / P_B then at temperature "0.5" the
probability ratio of the same 2 outcomes will be the square of the original
ratio

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bowmessage
Is the temperature semantically different from 'yet another input' into the
model? Asking as an ML noob.

