Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs).

Stochastic gradient methods have been a widespread practice in machine learning.

Positional encoding plays a crucial role in transformers, significantly impactingmodel performance andlength generalization. Prior research hasintroduced absolute positional encoding (APE) and relative positional encoding (RPE) to distinguish token positions in given sequences.

However, all known theoretical rates are worse than the rate of gradient descent, which synchronizes after every gradient step.

However, all known theoretical rates are worse than the rate of gradient descent, which synchronizes after every gradient step.