In the United States, health insurance is a critical component of individuals' ability to access health care.

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns.

In this work, we propose a memory-efficient zeroth-order optimizer ( MeZO), adapting the classical ZO-SGD method to operate in-place, thereby fine-tuning LMs with the same memory footprint as inference .

Explanations such as these that employ "even if..." reasoning,

CAM of top-2 predicted classes using mixup cross-entropy (MCE) and decoupled mixup (DM) loss.