So, while I'm not sure of the form this could take, I think it definitely makes sense to influence codes for k-mers that are close in terms of syntactic similarity. I've got a couple of papers to look at in terms of defining this:
The real problem here I think is I'm not sure exactly how to incorporate a regularization term into the stochastic proxy loss:
Each mini-batch of training data (*will be, but is not now) sampled randomly. The random sample might or might not grab probes with very similar k-mer profiles. In that case, how do you change the codes for the k-mers in this minibatch so that they get updated accordingly?
So, while I'm not sure of the form this could take, I think it definitely makes sense to influence codes for k-mers that are close in terms of syntactic similarity. I've got a couple of papers to look at in terms of defining this:
The real problem here I think is I'm not sure exactly how to incorporate a regularization term into the stochastic proxy loss:
*will be, but is not now) sampled randomly. The random sample might or might not grab probes with very similar k-mer profiles. In that case, how do you change the codes for the k-mers in this minibatch so that they get updated accordingly?