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Found 4 papers, 1 papers with code
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
A general approach to bridge the reality-gap
Employing machine learning models in the real world requires collecting large amounts of data, which is both time consuming and costly to collect.
Reducing audio membership inference attack accuracy to chance: 4 defenses
It is critical to understand the privacy and robustness vulnerabilities of machine learning models, as their implementation expands in scope.
Robust or Private? Adversarial Training Makes Models More Vulnerable to Privacy Attacks
Adversarial training was introduced as a way to improve the robustness of deep learning models to adversarial attacks.
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