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mark is a phd student at stanford university in the math department and here he will give us a summary of the recent paper that resolves that conjecture hopefully you will give us a quick introduction to what that conjecture is welcome mark we are excited to hear about your work take it away okay uh great thanks everyone so much for coming uh so today iamp;#39;ll tell you about a universal law of robustness via isoparametry and as was just mentioned itamp;#39;s joint with sebastian bubeck so the the motivation uh for this of course comes from adversarial examples and the kind of basic point of view is that we know two things we know that neural networks in practice essentially memorize the trading set so they have zero training error and they are vulnerable to small adversarial perturbations so whatamp;#39;s going on one way or another it seems like weamp;#39;re having difficulty training a robust memorizer so why is this so here are some plausible high-level explanations for why i
