Recent advances inface forgery techniques produce nearly visually untraceable deepfake videos, which could be leveraged with malicious intentions. As a result, researchers havebeen devoted todeepfakedetection.

Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands?

We address these flaws through a study of alternative self-supervised feature extractors, find that the semantic information encoded by individual networks strongly depends on their training procedure, and show that DINOv2-ViT -L/14 allows for much richer evaluation of generative models.

We establish an exact correspondence between the expressivity of GNNs using diverse activation functions and arithmetic circuits overrealnumbers.

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