pdeburen

4 ms·

1. 1.▲
   by pdeburen 9y ago

   > Their claim here is they can on the fly determine how important different samples are, and make sure samples that yield better results are in the chance more often than those with less importance. Isn't that the exact same idea as
2. 2.▲
   by pdeburen 9y ago

   Yeah they get you really hyped up first and drop the bomb at the end. Still quite impressive speedup, would be better though to show it on a benchmark where SVM's are used in practice
3. 3.▲
   by pdeburen 9y ago

   I'm not sure how the quote supports your argument. Adversial examples generalize well accross many different classifiers. Shallow NN's can be fooled just as well, it seems to be more of a problem of linear models in general. Appar
4. 4.▲
   by pdeburen 9y ago

   While it's true that these attacks work well on state-of-the-art models, there are defence strategies such as including adversial examples during training. Advanced defence strategies such as https://arxiv.org/abs/
5. 5.▲
   Efficient analysis of open ended survey questions(medium.com)

   1 points by pdeburen 9y ago|0 comments