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I'm knowingly being a pedant and I apologize for that but they don't quite say that, rather they come awfully close to doing so. And I'm being a pedant because

by varelse 12y ago

I'm knowingly being a pedant and I apologize for that but they don't quite say that, rather they come awfully close to doing so. And I'm being a pedant because of all the low information sorts claiming this is a refutation of deep neural networks (it's not, well at least not yet). "The above observations suggest that adversarial examples are somewhat universal and not just the results of overfitting to a particular model or to the specific selection of the training set. They also suggest that back-feeding adversarial examples to training might improve generalization of the resulting models." 20 years ago I did this for linear models for protein energetics (also known as knowledge-based potentials or force fields), adding the decoys then refitting the parameters ad nauseum. What I eventually arrived at was the invalidation of every single energy model and force field in use for protein energetics (yes I really reverse engineered just about everyone from Michael Levitt to George Rose to AMBER, CHARMM, and ECEPP). This was an unpublishable result according to my post-doc adviser at the time so it never got written up. In retrospect, he was utterly wrong. So I am really curious what would happen here if this were attempted with these much more complex models.

dekhn 12y ago
So, the other interesting paper which I failed to cite was this one: http://www.ncbi.nlm.nih.gov/pubmed/24265211 http://www.ncbi.nlm.nih.gov/pubmed/24265211 in which we showed that rosetta needed to include bond angle terms to accurately model some proteins. That said, I'm a bit surprised you found what you did about AMBER (and other force fields), or rather, that you didn't publish. The cornell et al force field was later acknowledged to have serious problems with protein folding, but a number of improvements have been made since then. Anyway, I would have happily published that result with you (I worked with Kollman, have worked with Baker and Pande, and desperately want to see the force fields improve using machine learning). There was a guy at BMS who was working on this back in the day ('99-2000) who was using ML and the AMBER folks trashed him because they believed the force field's transferrability from small molecules to proteins was valid (in many ways it was, but it got some key details wrong). Ifd you think there is a straightforward machine learning for force field problem that can dramatically improve ab initio folding with distbelief and exacycle, let me know. It shouldn't be hard to figure out my email address if you look at the papers I cited and do some basic set operations :-)
- dwiel 12y ago
  I talked to one of the authors of this paper at ICLR and he said that it wasn't really worth the time to compute the adversaries and train, though it did improve results. He said that in the time it took to generate adversaries and then train on them, the net was better off just training on more image data, since there is a near infinite supply of it. Perhaps if you didn't have an infinite dataset, this wouldn't apply. Also, the really interesting thing was that adversaries generated for one network topology/data set were still adversarial even for other network topologys/data sets, which might imply that the nature of the adversaries is universal rather than highly specific to that exact network trained

by varelse 12y ago

dekhn 12y ago

So, the other interesting paper which I failed to cite was this one: http://www.ncbi.nlm.nih.gov/pubmed/24265211 http://www.ncbi.nlm.nih.gov/pubmed/24265211 in which we showed that rosetta needed to include bond angle terms to accurately model some proteins. That said, I'm a bit surprised you found what you did about AMBER (and other force fields), or rather, that you didn't publish. The cornell et al force field was later acknowledged to have serious problems with protein folding, but a number of improvements have been made since then. Anyway, I would have happily published that result with you (I worked with Kollman, have worked with Baker and Pande, and desperately want to see the force fields improve using machine learning). There was a guy at BMS who was working on this back in the day ('99-2000) who was using ML and the AMBER folks trashed him because they believed the force field's transferrability from small molecules to proteins was valid (in many ways it was, but it got some key details wrong). Ifd you think there is a straightforward machine learning for force field problem that can dramatically improve ab initio folding with distbelief and exacycle, let me know. It shouldn't be hard to figure out my email address if you look at the papers I cited and do some basic set operations :-)

dwiel 12y ago
I talked to one of the authors of this paper at ICLR and he said that it wasn't really worth the time to compute the adversaries and train, though it did improve results. He said that in the time it took to generate adversaries and then train on them, the net was better off just training on more image data, since there is a near infinite supply of it. Perhaps if you didn't have an infinite dataset, this wouldn't apply. Also, the really interesting thing was that adversaries generated for one network topology/data set were still adversarial even for other network topologys/data sets, which might imply that the nature of the adversaries is universal rather than highly specific to that exact network trained