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I wrote a series of Markov chat simulators as a teenager. Often I used a simpler algorithm which ignored the probability weight (all out-links, once learned, g
by phaedrus 7y ago
I wrote a series of Markov chat simulators as a teenager. Often I used a simpler algorithm which ignored the probability weight (all out-links, once learned, given equal probability). These version performed subjectively as well as, if not better, than the versions which tracked the weight of links. I'm not surprised therefore that weight agnostic neural networks can work, too.
- meowface 7y agoI think it may not be a great comparison. N-grams (of words) of human speech/writing are way more deterministic than the kinds of things ML usually tries to tackle, I think. If you write the word "because", then "of", "the", or some pronoun are all extremely safe bets for the next word, regardless of their recorded probabilities. I imagine you could also totally randomize the probabilities and not see any issues. But I'm no expert and hardly even an amateur, so maybe it is a similar kind of thing here with ML. And I know randomized optimization is a big thing in ML, though I'm not sure to what extent that could be analogized with randomizing Markov model probabilities.