Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs.

Theapproximationisupdatedin iterations based on some special formulas from the previous variation.

Traditional online algorithms typically assume a small bid setting, where the maximumbid-to-budgetratio(κ)isinfinitesimallysmall.

Manyexisting methods generate captions using sequence models that predict words in a left-to-right order.

The optimization landscape of each task consists of a deep valley, a property that has been observed in neural network optimization landscapes [22], and the bottom ofeachvalleyischaracterized by high positive curvature and large differences in the task gradient magnitudes.