165 citations · 455 across the 48 of their papers we have counts for
8 papers · 1 filter
Adversarial Attacks, Regression, and Numerical Stability Regularization
Andre T. Nguyen, Edward Raff
Adversarial attacks against neural networks in a regression setting are a critical yet understudied problem. In this work, we advance the state of the art by investigating adversar…
Growing and Retaining AI Talent for the United States Government
Edward Raff
Artificial Intelligence and Machine Learning have become transformative to a number of industries, and as such many industries need for AI talent is increasing the demand for indiv…
Gradient Reversal Against Discrimination
Edward Raff, Jared Sylvester
No methods currently exist for making arbitrary neural networks fair. In this work we introduce GRAD, a new and simplified method to producing fair neural networks that can be used…
What About Applied Fairness?
Jared Sylvester, Edward Raff
Machine learning practitioners are often ambivalent about the ethical aspects of their products. We believe anything that gets us from that current state to one in which our system…
Static Malware Detection & Subterfuge: Quantifying the Robustness of Machine Learning and Current Anti-Virus
William Fleshman, Edward Raff, Richard Zak +2
As machine-learning (ML) based systems for malware detection become more prevalent, it becomes necessary to quantify the benefits compared to the more traditional anti-virus (AV) s…
Non-Negative Networks Against Adversarial Attacks
William Fleshman, Edward Raff, Jared Sylvester +2
Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available. We make progress toward this probl…