Forexample,thecommunication probabilities canbe31 chosen as inversely proportional to the edge delay or to the degree of their incident nodes, but this choice is highly32 dependent onthesetting. Yet, we believe that the scale of the experiments is already quite significant.

In disciplines such as chemistry, physics, and network science, graphs are a convenient and well-studied mathematical object to represent such interacting entities and their attributes.

Please see https://arxiv.org/abs/1910.12478 for the full version of this paper.

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The techniques for theℓ2-perturbation do not easily transfer to theℓ -case (Leino et al., 2021). One critical step is to measure the Lipschitz constant more precisely and efficiently.

The techniques for theℓ2-perturbation do not easily transfer to theℓ -case (Leino et al., 2021). One critical step is to measure the Lipschitz constant more precisely and efficiently.

In this paper, we propose MotionTTT a deep learning-based test-time-training (TTT) method for accurate motion estimation.

Wespanthe distilled representation of the synthetic domain tothe real domain by also using real training data to remedy the mode collapse of GANs and boost the student network performance in a real domain.

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