Humans can often quickly and efficiently solve complex new learning tasks given only a small set of examples. In contrast, modern artificially intelligent systems often require thousands or millions of observations in order to solve even the most basic tasks. Meta-learning aims to resolve this issue by leveraging past experiences from similar learning tasks to embed the appropriate inductive biases into the learning system. Historically methods for meta-learning components such as optimizers, parameter initializations, and more have led to significant performance increases. This thesis aims to explore the concept of meta-learning to improve performance, through the often-overlooked component of the loss function. The loss function is a vital component of a learning system, as it represents the primary learning objective, where success is determined and quantified by the system's ability to optimize for that objective successfully.

翻译：人类通常能够仅凭少量示例就快速高效地解决复杂的新学习任务。相比之下，现代人工智能系统往往需要成千上万甚至数百万次观察才能解决最基本的任务。元学习旨在通过利用相似学习任务中的过往经验，将适当的归纳偏置嵌入学习系统，从而解决这一问题。历史上，针对优化器、参数初始化等元学习组件的方法已带来显著的性能提升。本论文旨在探索通过常被忽视的损失函数组件，利用元学习概念来提升性能。损失函数是学习系统的关键组成部分，它代表了主要的学习目标；系统能否成功优化该目标，决定了其成败并提供了量化标准。