3 ms·
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 mod
by mnky9800n 2y 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.
- magospietato 2y agoI'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.
- Jerrrry 2y agoSieve 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.
- QuadmasterXLII 2y agothats just mixture of experts
- mnky9800n 2y agoi 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.
- QuadmasterXLII 2y agoWell, that depends in whether you keep training it
- mnky9800n 2y agoperhaps they should always be training and never static. haha. i allegedly grow wiser in my age, why not neural networks?