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Naive question: is the exponential increase in performance talked about in this article unique to neural nets? Or are there other techniques for writing classif
by nicklaf 9y ago
Naive question: is the exponential increase in performance talked about in this article unique to neural nets? Or are there other techniques for writing classifiers that yield the same performance increase, given advances in hardware?
- mholt 9y agoNeural networks are function approximators. So if you 1) know an algorithm that is really computationally complex but not highly random and 2) have a lot of inputs and outputs of that algorithm, you can usually train a neural network to approximate a closed-form formula of the algorithm. It boils down to a bunch of matrix-multiplies and some standard non-linear functions in between.
- posterboy 9y agois that anything close to like polynomial fitting? What with PTIME and NP-Completion?
- mholt 9y agoKind of - but instead of computational complexity in the "NP" sense, you have lots of data. It's often so hard to get good training data that the cost of just waiting out a big algorithm to finish can be cheaper. So you have to weigh that.
- posterboy 9y agowell sure, you said as much before. But I was also thinking, what if P~=NP, in the sense that any function can be approximated by a polynomial of sufficiently high degree.
- jawbone3 9y agoNo, you can of course write some simple stretchy model that fits fast. The thing with NNs is that you don't have to domuch work to get a good fast model, you use cpu cores for that instead.