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Haven't read the article, but wanted to ask a question: Once the data is mined and the lessons learnt, isn't that it? You don't need the massive learning algori

Haven't read the article, but wanted to ask a question: Once the data is mined and the lessons learnt, isn't that it? You don't need the massive learning algorithm, just the little 'rules reading' algorithm? Is this a thing? Something that takes a convolutional neural network and spits out a little app?

derefr 11y ago
Most of the interesting things neural networks do involve online learning. For a simple non-NN analogy, think of spam detection: you want to be able to correct misclassifications, such that it won't make them again. This requires more effort than it sounds: it's not enough to just add the one piece of weighted evidence to the corpus and re-run the algorithm, because that won't necessarily make the filter spit out the new correct answer in the case of the original. Just like there's overfitting, there's also underfitting, and the naive training method results in underfitting. Thus, what you tend to want is something more like a garbage collector: a process constantly running in a background thread, gradually retraining the system. At any given point, the system will answer questions using an MVCC-like point-in-time view of its beliefs, while those beliefs are getting played with and re-evaluated elsewhere. Also, every time the NN changes its mind, there will be a subset of non-training samples it has already seen, that it will now classify differently than it originally did when it saw them. Continuously going back and amending its judgements on these is usually helpful.
- minthd 11y ago
  How large is this machinery for online-learning ?
soup10 11y ago
I think it depends on the algorithm. Something like siri trains on a larger data set with better hardware and uses a reduced version on the phone hardware. I think most neural networks can be reduced and compressed into a 'read only' format with limited or no new training, but it's a secondary problem.
nl 11y ago
If I understand what you are talking about, then yes. For example, offline speech recognition in Android phones uses a CNN that was trained on Google's GPU cluster[1] [1] I can't find the good reference of this, but Slide 31 of this is outdated, but kind of says it (you aren't training a 2.7M parameter CNN on a phone). http://www.cs.nyu.edu/~eugenew/asr13/lecture_14.pdf http://www.cs.nyu.edu/~eugenew/asr13/lecture_14.pdf

derefr 11y ago

Most of the interesting things neural networks do involve online learning. For a simple non-NN analogy, think of spam detection: you want to be able to correct misclassifications, such that it won't make them again. This requires more effort than it sounds: it's not enough to just add the one piece of weighted evidence to the corpus and re-run the algorithm, because that won't necessarily make the filter spit out the new correct answer in the case of the original. Just like there's overfitting, there's also underfitting, and the naive training method results in underfitting. Thus, what you tend to want is something more like a garbage collector: a process constantly running in a background thread, gradually retraining the system. At any given point, the system will answer questions using an MVCC-like point-in-time view of its beliefs, while those beliefs are getting played with and re-evaluated elsewhere. Also, every time the NN changes its mind, there will be a subset of non-training samples it has already seen, that it will now classify differently than it originally did when it saw them. Continuously going back and amending its judgements on these is usually helpful.

minthd 11y ago
How large is this machinery for online-learning ?

soup10 11y ago

I think it depends on the algorithm. Something like siri trains on a larger data set with better hardware and uses a reduced version on the phone hardware. I think most neural networks can be reduced and compressed into a 'read only' format with limited or no new training, but it's a secondary problem.

nl 11y ago

If I understand what you are talking about, then yes. For example, offline speech recognition in Android phones uses a CNN that was trained on Google's GPU cluster[1] [1] I can't find the good reference of this, but Slide 31 of this is outdated, but kind of says it (you aren't training a 2.7M parameter CNN on a phone). http://www.cs.nyu.edu/~eugenew/asr13/lecture_14.pdf http://www.cs.nyu.edu/~eugenew/asr13/lecture_14.pdf