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>I mean one could imagine a deterministic model that couldn't be "tricked". The models in reference are deterministic, most models for classification/regressio
by alexbeloi 9y ago
>I mean one could imagine a deterministic model that couldn't be "tricked".
The models in reference are deterministic, most models for classification/regression are deterministic.
Adversarial attacks are showing that the models are chaotic (in the dynamical systems sense), they're very sensitive to their inputs.
edit: Not all models have this issue, it's been shown that all the typical image based convolutional neural networks are susceptible to this issue. My guess is that it's a more general problem of high dimensional inputs.
- joe_the_user 9y agoYeah, my complaint is that they're using the generic phrase "machine learning" for specific deep learning and other neural net methods. Certainly, a wide class of "black box" algorithms can be fooled, mostly because they extrapolate in surprising and paradoxical ways.
- xapata 9y agoWe've known for decades that NNs are prone to overfit. This is just another example of it. With great variance comes great ... uh ... susceptibility to unusual future inputs?