Our initial investigation identifies which distributions accurately characterize the feature space, subsequently leveraging this priori to guide the alignment of the weakly supervised embeddings. Specifically, we analyze the superiority of the mixture of von Mises-Fisher distributions (moVMF) among several common distribution candidates.

In addition, we examine two common computation frameworks for this problem, i.e., population-based training (PBT) and iterative learning

We then apply the framework to the challenging practical setting where confounding factors (that induce spurious correlations) are observable only on asmall fraction of data.

Neolithic period to the Industrial Revolution and includes information reviewed and assembled by history experts and graduate research assistants.

Solving complex planning problems has been a long-standing challenge in computer science.

In this work, we introduce WinoGAViL: an online game of vision-andlanguage associations (e.g., betweenwerewolves and a full moon), used as a dynamic evaluation benchmark.