In this work, we introduce WinoGA ViL: an online game of vision-and-language associations (e.g., between werewolves and a full moon), used as a dynamic evaluation benchmark.
We then apply the framework to the challenging practical setting where confounding factors (that induce spurious correlations) are observable only on a small fraction of data.
The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's (ESA) Phi-Lab as part of the ESA-funded
Why do many modern neural-network-based graph generative models fail to reproduce typical real-world network characteristics, such as high triangle density?
Why do many modern neural-network-based graph generative models fail to reproduce typical real-world network characteristics, such as high triangle density?