Wefind that while scaling training data ormodel size can boost many vision-language model capabilities, scaling offers little benefit for reasoning or relations. Surprisingly, we also discover today's best VLMs struggle on simple digit recognition and counting tasks, e.g. MNIST, which much simpler networks can solve.

Missing values in real-world data pose a significant and unique challenge to algorithmic fairness.

Many important real-world scenarios involve data structured as graphs.

Recently, extensive literature has shown that the relationship between SC and FC is complex and not a simple one-to-one mapping.