Computational modeling and machine learning (ML) have led to an acceleration of scientific innovation in diverse areas, ranging from drug design to robotics to material science.

A prime example is the recently debated "reversal curse", which surfaces when models, having been trained on the fact "A is B", struggle to generalize this knowledge to infer that "B is A ".

Deep neural networks are the state-of-the-art for various applications. However,one of the biggest challenges facing them is the lack of labeled data to train these complex networks.