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Try to derive e.g. a face detector from bayes theorem. You immediately arrive at computationally intractable sums/integrals. Yet, we have super-human image clas
by reader5000 9y ago
Try to derive e.g. a face detector from bayes theorem. You immediately arrive at computationally intractable sums/integrals. Yet, we have super-human image classifiers. Therefore, bayes theorem is obsolete. Sure, you can try to retrofit bayes theorem on top of a neural net, but who cares?
- achompas 9y ago> You immediately arrive at computationally intractable sums/integrals. So we instead sample from that posterior. Unless you think MCMC is also obsolete, in which case I’ll see myself out.
- reader5000 9y agoYou'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- reader5000 9y agoWell certainly, but interpretability is obsolete.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. :)