In this work, we propose data-efficient and interpretable machine learning models for learning to solve numeric planning tasks.

Our preliminary experiments have shown that the effectiveness of the existing methods often drastically decrease or even fail when tackling lip-syncing videos.

In this work, we study the effect of occlusion on video action recognition.

Lipschitzness, which we make precise in Section 2 and in Assumption 3.1.