Hello everyone, I've been wondering if it's possible to transfer knowledge from one trained HTM network to another.
For instance, let's say there's a trained language model on every user's phone, and there's a global language model on the cloud. The distributed client models were initially copies of the cloud model but further trained on the user's own data, thus personalizing them. Then, you train the cloud model with more public textual training data, and it learns new patterns (new vocabulary, new phrases, etc.). What would be the best way to transfer the new knowledge from the cloud model to the client models? Since the internal connections between neurons don't translate between models, I imagine that only the externally facing layers (the input and output layers) are useful in transferring data. So then one way would be to have the cloud model generate patterns at the output layer, and feed that to the client model's input layer. Kind of like the cloud model is "talking", and the client model is "listening". After all, this is the only effective way to transfer knowledge between humans, since we can't connect our brains to each other directly. But it's at least faster than training the client models directly on the raw training data, because the cloud model can compress the patterns and transfer them more efficiently. That's just one idea, and I'm not even sure how exactly that would work. I pretty much just thought of it analogous to human communication. Are there better ways with HTMs? Thanks, Chetan
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