5 citations · 6 across the 4 of their papers we have counts for
4 papers
Identifying Human Strategies for Generating Word-Level Adversarial Examples
Maximilian Mozes, Bennett Kleinberg, Lewis D. Griffin
Adversarial examples in NLP are receiving increasing research attention. One line of investigation is the generation of word-level adversarial examples against fine-tuned Transform…
Self-Supervised Losses for One-Class Textual Anomaly Detection
Kimberly T. Mai, Toby Davies, Lewis D. Griffin
Current deep learning methods for anomaly detection in text rely on supervisory signals in inliers that may be unobtainable or bespoke architectures that are difficult to tune. We…
Contrasting Human- and Machine-Generated Word-Level Adversarial Examples for Text Classification
Maximilian Mozes, Max Bartolo, Pontus Stenetorp +2
Research shows that natural language processing models are generally considered to be vulnerable to adversarial attacks; but recent work has drawn attention to the issue of validat…
Brittle Features May Help Anomaly Detection
Kimberly T. Mai, Toby Davies, Lewis D. Griffin
One-class anomaly detection is challenging. A representation that clearly distinguishes anomalies from normal data is ideal, but arriving at this representation is difficult since…