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(68%) Stephen Obadinma Xiaodan Zhu Hongyu Guo A backdoor attack against link prediction tasks with graph neural networks. (98%) Hui Zeng Biwei Chen Anjie Peng Calibration Attack: A Framework For Adversarial Attacks Targeting Calibration. Enhancing targeted transferability via feature space fine-tuning. This data I'd be happy to hear from you what it was. Simplistic (but reasonably well calibrated)īag-of-words classifier believes the given paper The new un-verified entries will have a probability indicated that my Then that I'll remove the ones that aren't related toįalse positives on the most recent few entries. I do this filtering roughly twice a week, and it's Get a chance to manually filter through them. This list automatically updates with new papers, even before I Send me an email if something is wrong and I'll correct it.Īs a result, this list is completely un-filtered.Įverything that mainly presents itself as an adversarialĮxample paper is listed here I pass no judgement of quality.įor a curated list of papers that I think are excellent andĪdversarial Machine Learning Reading List. Judgement calls as to whether or not any given paper is These criteria (and are about something different instead), I also may have included papers that don't match Or extensively uses adversarial examples.ĭue to the sheer quantity of papers, I can't guarantee

Is that it is primarily a paper about adversarial examples, The only requirement I used for selecting papers for this list Papers for the last few years, and realized it may be helpful

I have been somewhat religiously keeping track of these Where we have seen massive growth in the number of papers It can be hard to stay up-to-date on the published papers in

Download the new for mac Parted Magic 2023.08.22