9 citations · 11 across the 3 of their papers we have counts for
4 papers
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
Marina Fomicheva, Shuo Sun, Erick Fonseca +7
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE). The dataset contains eleven language pairs, with human labe…
A general approach to bridge the reality-gap
Michael Lomnitz, Zigfried Hampel-Arias, Nina Lopatina +1
Employing machine learning models in the real world requires collecting large amounts of data, which is both time consuming and costly to collect. A common approach to circumvent t…
Reducing audio membership inference attack accuracy to chance: 4 defenses
Michael Lomnitz, Nina Lopatina, Paul Gamble +4
It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope. In membership inference attacks, adver…
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks
Felipe A. Mejia, Paul Gamble, Zigfried Hampel-Arias +4
Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks. This training method improves robustness against adversarial…