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Not an example from deep learning, but [1] also demonstrates that Bayesian systems also have similar problems with sensitivity to initial conditions that are qu

by hyperion2010 12y ago

Not an example from deep learning, but [1] also demonstrates that Bayesian systems also have similar problems with sensitivity to initial conditions that are quite similar. It is also rather striking that these DLNs seem to be tricked by what we would typically think of as noise. 1. http://arxiv.org/abs/1308.6306 http://arxiv.org/abs/1308.6306

hyperbovine 12y ago
It is not so striking when you consider how the typical published "result" in deep learning is obtained -- spend a few months turning all the different knobs that these models have to offer (while possibly inventing a few new ones) until its performance meets or beats the current state of the art on MNIST, CIFAR, and related benchmarks. Which is to say, these models heavily overfit to a few datasets; it should not come as a big surprise when they generalize poorly. What happens now will be that this perturbed dataset will be added to the standard training corpus and the DLNs will become robust to this effect. Then someone will figure out new way to mess them up, and the process will repeat.
- anonymousDan 12y ago
  Would you classify this as progress?
- wantrepeneur 12y ago
  I don't know what papers you are reading, but you seem to have a very distorted view of the literature. How do results on image net, production speech recognition datasets, language modeling, and high resolution satellite images fit the pattern you allege? Once again HN comment quality is depressingly low when it comes to machine learning topics. But I guess to be fair the description in the original link is very confused as well and misunderstands the conclusions of the paper.
apw 12y ago
I thought the Owhadi et al. paper was about model mispecification; i.e., the true model is not in the hypothesis space. That's pretty fundamentally different--and far less of a problem--than gradient descent's "sensitivity to initial conditions".

hyperbovine 12y ago

It is not so striking when you consider how the typical published "result" in deep learning is obtained -- spend a few months turning all the different knobs that these models have to offer (while possibly inventing a few new ones) until its performance meets or beats the current state of the art on MNIST, CIFAR, and related benchmarks. Which is to say, these models heavily overfit to a few datasets; it should not come as a big surprise when they generalize poorly. What happens now will be that this perturbed dataset will be added to the standard training corpus and the DLNs will become robust to this effect. Then someone will figure out new way to mess them up, and the process will repeat.

anonymousDan 12y ago
Would you classify this as progress?
wantrepeneur 12y ago
I don't know what papers you are reading, but you seem to have a very distorted view of the literature. How do results on image net, production speech recognition datasets, language modeling, and high resolution satellite images fit the pattern you allege? Once again HN comment quality is depressingly low when it comes to machine learning topics. But I guess to be fair the description in the original link is very confused as well and misunderstands the conclusions of the paper.

apw 12y ago

I thought the Owhadi et al. paper was about model mispecification; i.e., the true model is not in the hypothesis space. That's pretty fundamentally different--and far less of a problem--than gradient descent's "sensitivity to initial conditions".