Well, in your work, whay benefit did you get from it? And do you think it would be beneficial today combined with modern techniques? Or obsoleted by other technqiue?
(I ask because I'm finding many old techniques are still good or could be mixed with deep learning.)
It was not bad, but I had trouble scaling it to the 1B set. Mostly because I have not enough time.
I do hold same mindset as yours, that many old techniques are misunderstood or underapplied. For example, decision trees, in my experiments, allow for bit-length-per-byte comparable to LSTM (lstm-compress or LSTM in nncp experiments): https://github.com/thesz/codeta
Well, in your work, whay benefit did you get from it? And do you think it would be beneficial today combined with modern techniques? Or obsoleted by other technqiue?
(I ask because I'm finding many old techniques are still good or could be mixed with deep learning.)