Critical in this context is the prior knowledge accumulated from related tasks.

Whentheinputs associated Ex. 1), theterms

We then integrate these findings toobtain anoverallperformance guarantee forourmetalearning algorithm.

Inthiswork,wepropose anovelCrossAttention Network to address the challenging problems in few-shot classification.

MostNASmethodstodayfocusona single task with afixedhardwaresystem, yetreal-life model deployments covering multiple tasks andvarioushardwareplatforms willsignificantly prolong thisprocess.

We illustrate these bounds in the logistic regression and neural network settings. In contrast, we establish settings where learning one representation for all tasks (i.e. using a "frozen representation" objective) fails. Notably, any such algorithm cannot outperform directly learning the target task with no other information, in the worst case.