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You're right, but a) you have comp efficiency issues with MCMC, and b) just empirically MCMC models don't work as well as gradient descent + NN for many tasks.
by reader5000 9y ago
You're right, but a) you have comp efficiency issues with MCMC, and b) just empirically MCMC models don't work as well as gradient descent + NN for many tasks.
- achompas 9y agoAnd you don't have computational efficiency issues with NNs? We're also ignoring the benefits of a posterior distribution, which is useful for understanding the data-generating process.
- reader5000 9y agoYeah of course. I can't explain to you why NNs outperform bayesian approaches, probably just NNs are capturing the correct type of prior for vision/language tasks. And yeah bayesian models are more interpretable but when you have millions of latent variables I'm not sure interpretability is a thing.
- achompas 9y agoYep, we arrived at my larger point: if you care about interpretability, NNs are horrible and Bayesian techniques are pretty damn great.
- reader5000 9y agoWell certainly, but interpretability is obsolete.
- achompas 9y agoNow you're just trolling. :)