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Let’s evaluate with Macro F1: what can go wrong?

Juri Opitz, Sebastian Burst

Earlier this year we got slightly puzzled about how to best calculate the “macro F1” score to measure the performance of a classifier.
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Adversarial Training

Sebastian Burst

In the past two years, machine learning, particularly neural computer vision and NLP, have seen a tremendous rise in popularity of all things adversarial. In this blog post I will give an overview of the two most popular training methods that are commonly referred to as adversarial: Injecting adversarial examples (1) and min-max optimization (2). After showcasing how they are applied in NLP I will compare them and examine ways to combine them (3).