Please find the link for my GitHub repository, with a working example for encoding visual data ( images) as input to the HTM system. I was able to successfully encode handwritten digits and inputs to SP, using an autoencoder. Finally, train SP to find all related images which have to overlap SDR.
I’m sure taking a DL latent vector and turning it into a binary vector to feed to HTM is discussed/practiced in this forum before.
(Like this one: Proof of concept: Trainable universal encoder architecture)
And I’m doing something similar at the moment as well.
I’ve actually added k-winner layer to the bottleneck of the network and also had a loss function to enforce sparsity over time to make it get along with HTM better.
I’ve used this tactic to encode English words to binary vectors:
I was thinking the same think with respect to NLP, with transformer architecture we would be able to encode, entire language corpus ( latent representation) as SP SDR or TP SDR. I came across this on github. https://github.com/alexyalunin/transformer-autoencoder.
Very interesting! NLP is definitely one of the under explored potentials that HTM could handle.
I wonder what an HTM system can do with properly encoded NLP data.