Adversarially Robust Neural Style Transfer

A figure in Ilyas, et. al. that struck me as particularly interesting was the following graph showing a correlation between adversarial transferability between architectures and their tendency to learn similar non-robust features. Adversarial transferability vs test accuracy of different architectures trained on ResNet-50′s non-robust features. One way to interpret this graph is that it shows […]

13 mins read

Billionaire Bill Ackman Has 51% of His Hedge Fund’s $14.4 Billion Portfolio Invested in Just 3 Exceptional Stocks

Ackman’s best ideas still look attractive at today’s prices. Bill Ackman likes to keep his hedge fund, Pershing Square Capital, invested in just a few high-conviction companies. Indeed, it’s hard to generate market-beating returns if your investments are spread so thin your portfolio looks pretty similar to the overall stock market. But Ackman and his […]

6 mins read

That Network Traffic Looks Legit, But it Could be Hiding a Serious Threat

Jul 02, 2025The Hacker NewsNetwork Security / Threat Detection With nearly 80% of cyber threats now mimicking legitimate user behavior, how are top SOCs determining what’s legitimate traffic and what is potentially dangerous? Where do you turn when firewalls and endpoint detection and response (EDR) fall short at detecting the most important threats to your […]

6 mins read

Learning from Incorrectly Labeled Data

Section 3.2 of Ilyas et al. (2019) shows that training a model on only adversarial errors leads to non-trivial generalization on the original test set. We show that these experiments are a specific case of learning from errors. We start with a counterintuitive result — we take a completely mislabeled training set (without modifying the inputs) and […]

8 mins read