Comment by mnky9800n
3 days ago
Why not, as each new task comes up, and then weights are revalued, save those weights and keep them for reference as priors for similar future tasks? As the model is exposed to new data the average of the set of priors of things the model thinks is similar might move closer to the posterior making the model quicker and more able to arrive at good outcomes. I suppose storage might be an issue.
I'm wondering if you could fine tune the model on an aggregate of a temporal slice of revalued weights? Something analogous to REM sleep's involvement in embedding the days events into long term memory.
Sieve the temporary backprop interim weights as a function of its loss of varentrophy relative to its place in the revalued weights.
Remove the bottom weights dynamically based on the local gradient in varentrophy so that internal dissonance ("doubt") can be selected against.
"Preference Optimization" but with more opportunities for meta-optimization.
thats just mixture of experts
i thought mixture of experts didn't update itself with new sets of weights and was just a collection of already trained networks/weights? I could be wrong.
Well, that depends in whether you keep training it
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